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arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Materials on this site are not peer-reviewed by arXiv.

arXiv is a free distribution service and an open-access archive for scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Materials on this site are not peer-reviewed by arXiv.

Stay up to date with what is happening at arXiv on our blog.

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21 Legit Research Databases for Free Journal Articles in 2024

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Written by Scribendi

Has this ever happened to you? While looking for websites for research, you come across a research paper site that claims to connect academics to a peer-reviewed article database for free.

Intrigued, you search for keywords related to your topic, only to discover that you must pay a hefty subscription fee to access the service. After the umpteenth time being duped, you begin to wonder if there's even such a thing as free journal articles.

Subscription fees and paywalls are often the bane of students and academics, especially those at small institutions who don't provide access to many free article directories and repositories.

Whether you're working on an undergraduate paper, a PhD dissertation, or a medical research study, we want to help you find tools to locate and access the information you need to produce well-researched, compelling, and innovative work.

Below, we discuss why peer-reviewed articles are superior and list out the best free article databases to use in 2024.

Download Our Free Research Database Roundup PDF

Why peer-reviewed scholarly journal articles are more authoritative.

Determining what sources are reliable can be challenging. Peer-reviewed scholarly journal articles are the gold standard in academic research. Reputable academic journals have a rigorous peer-review process.

The peer review process provides accountability to the academic community, as well as to the content of the article. The peer review process involves qualified experts in a specific (often very specific) field performing a review of an article's methods and findings to determine things like quality and credibility.

Peer-reviewed articles can be found in peer-reviewed article databases and research databases, and if you know that a database of journals is reliable, that can offer reassurances about the reliability of a free article. Peer review is often double blind, meaning that the author removes all identifying information and, likewise, does not know the identity of the reviewers. This helps reviewers maintain objectivity and impartiality so as to judge an article based on its merit.

Where to Find Peer-Reviewed Articles

Peer-reviewed articles can be found in a variety of research databases. Below is a list of some of the major databases you can use to find peer-reviewed articles and other sources in disciplines spanning the humanities, sciences, and social sciences.

What Are Open Access Journals?

An open access (OA) journal is a journal whose content can be accessed without payment. This provides scholars, students, and researchers with free journal articles. OA journals use alternate methods of funding to cover publication costs so that articles can be published without having to pass those publication costs on to the reader.

Some of these funding models include standard funding methods like advertising, public funding, and author payment models, where the author pays a fee in order to publish in the journal. There are OA journals that have non-peer-reviewed academic content, as well as journals that focus on dissertations, theses, and papers from conferences, but the main focus of OA is peer-reviewed scholarly journal articles.

The internet has certainly made it easier to access research articles and other scholarly publications without needing access to a university library, and OA takes another step in that direction by removing financial barriers to academic content.

Choosing Wisely

Features of legitimate oa journals.

There are things to look out for when trying to decide if a free publication journal is legitimate:

Mission statement —The mission statement for an OA journal should be available on their website.

Publication history —Is the journal well established? How long has it been available?

Editorial board —Who are the members of the editorial board, and what are their credentials?

Indexing —Can the journal be found in a reliable database?

Peer review —What is the peer review process? Does the journal allow enough time in the process for a reliable assessment of quality?

Impact factor —What is the average number of times the journal is cited over a two-year period?

Features of Illegitimate OA Journals

There are predatory publications that take advantage of the OA format, and they are something to be wary of. Here are some things to look out for:

Contact information —Is contact information provided? Can it be verified?

Turnaround —If the journal makes dubious claims about the amount of time from submission to publication, it is likely unreliable.

Editorial board —Much like determining legitimacy, looking at the editorial board and their credentials can help determine illegitimacy.

Indexing —Can the journal be found in any scholarly databases?

Peer review —Is there a statement about the peer review process? Does it fit what you know about peer review?

How to Find Scholarly Articles

Identify keywords.

Keywords are included in an article by the author. Keywords are an excellent way to find content relevant to your research topic or area of interest. In academic searches, much like you would on a search engine, you can use keywords to navigate through what is available to find exactly what you're looking for.

Authors provide keywords that will help you easily find their article when researching a related topic, often including general terms to accommodate broader searches, as well as some more specific terms for those with a narrower scope. Keywords can be used individually or in combination to refine your scholarly article search.

Narrow Down Results

Sometimes, search results can be overwhelming, and searching for free articles on a journal database is no exception, but there are multiple ways to narrow down your results. A good place to start is discipline.

What category does your topic fall into (psychology, architecture, machine learning, etc.)? You can also narrow down your search with a year range if you're looking for articles that are more recent.

A Boolean search can be incredibly helpful. This entails including terms like AND between two keywords in your search if you need both keywords to be in your results (or, if you are looking to exclude certain keywords, to exclude these words from the results).

Consider Different Avenues

If you're not having luck using keywords in your search for free articles, you may still be able to find what you're looking for by changing your tactics. Casting a wider net sometimes yields positive results, so it may be helpful to try searching by subject if keywords aren't getting you anywhere.

You can search for a specific publisher to see if they have OA publications in the academic journal database. And, if you know more precisely what you're looking for, you can search for the title of the article or the author's name.

Determining the Credibility of Scholarly Sources

Ensuring that sources are both credible and reliable is crucial to academic research. Use these strategies to help evaluate the usefulness of scholarly sources:

Peer Review : Look for articles that have undergone a rigorous peer-review process. Peer-reviewed articles are typically vetted by experts in the field, ensuring the accuracy of the research findings.

Tip: To determine whether an article has undergone rigorous peer review, review the journal's editorial policies, which are often available on the journal's website. Look for information about the peer-review process, including the criteria for selecting reviewers, the process for handling conflicts of interest, and any transparency measures in place.

Publisher Reputation : Consider the reputation of the publisher. Established publishers, such as well-known academic journals, are more likely to adhere to high editorial standards and publishing ethics.
Author Credentials : Evaluate the credentials and expertise of the authors. Check their affiliations, academic credentials, and past publications to assess their authority in the field.
Citations and References : Examine the citations and references provided in the article. A well-researched article will cite credible sources to support its arguments and findings. Verify the accuracy of the cited sources and ensure they are from reputable sources.
Publication Date : Consider the publication date of the article. While older articles may still be relevant, particularly in certain fields, it is best to prioritize recent publications for up-to-date research and findings.
Journal Impact Factor : Assess the journal's impact factor or other metrics that indicate its influence and reputation within the academic community. Higher impact factor journals are generally considered more prestigious and reliable.

Tip: Journal Citation Reports (JCR), produced by Clarivate Analytics, is a widely used source for impact factor data. You can access JCR through academic libraries or directly from the Clarivate Analytics website if you have a subscription.

Peer Recommendations : Seek recommendations from peers, mentors, or professors in your field. They can provide valuable insights and guidance on reputable sources and journals within your area of study.
Cross-Verification : Cross-verify the information presented in the article with other credible sources. Compare findings, methodologies, and conclusions with similar studies to ensure consistency and reliability.

By employing these strategies, researchers can confidently evaluate the credibility and reliability of scholarly sources, ensuring the integrity of their research contributions in an ever-evolving landscape.

The Top 21 Free Online Journal and Research Databases

Navigating OA journals, research article databases, and academic websites trying to find high-quality sources for your research can really make your head spin. What constitutes a reliable database? What is a useful resource for your discipline and research topic? How can you find and access full-text, peer-reviewed articles?

Fortunately, we're here to help. Having covered some of the ins and outs of peer review, OA journals, and how to search for articles, we have compiled a list of the top 21 free online journals and the best research databases. This list of databases is a great resource to help you navigate the wide world of academic research.

These databases provide a variety of free sources, from abstracts and citations to full-text, peer-reviewed OA journals. With databases covering specific areas of research and interdisciplinary databases that provide a variety of material, these are some of our favorite free databases, and they're totally legit!

CORE is a multidisciplinary aggregator of OA research. CORE has the largest collection of OA articles available. It allows users to search more than 219 million OA articles. While most of these link to the full-text article on the original publisher's site, or to a PDF available for download, five million records are hosted directly on CORE.

CORE's mission statement is a simple and straightforward commitment to offering OA articles to anyone, anywhere in the world. They also host communities that are available for researchers to join and an ambassador community to enhance their services globally. In addition to a straightforward keyword search, CORE offers advanced search options to filter results by publication type, year, language, journal, repository, and author.

CORE's user interface is easy to use and navigate. Search results can be sorted based on relevance or recency, and you can search for relevant content directly from the results screen.

Collection : 219,537,133 OA articles

Other Services : Additional services are available from CORE, with extras that are geared toward researchers, repositories, and businesses. There are tools for accessing raw data, including an API that provides direct access to data, datasets that are available for download, and FastSync for syncing data content from the CORE database.

CORE has a recommender plug-in that suggests relevant OA content in the database while conducting a search and a discovery feature that helps you discover OA versions of paywalled articles. Other features include tools for managing content, such as a dashboard for managing repository output and the Repository Edition service to enhance discoverability.

Good Source of Peer-Reviewed Articles : Yes

Advanced Search Options : Language, author, journal, publisher, repository, DOI, year

2. ScienceOpen

Functioning as a research and publishing network, ScienceOpen offers OA to more than 74 million articles in all areas of science. Although you do need to register to view the full text of articles, registration is free. The advanced search function is highly detailed, allowing you to find exactly the research you're looking for.

The Berlin- and Boston-based company was founded in 2013 to "facilitate open and public communications between academics and to allow ideas to be judged on their merit, regardless of where they come from." Search results can be exported for easy integration with reference management systems.

You can also bookmark articles for later research. There are extensive networking options, including your Science Open profile, a forum for interacting with other researchers, the ability to track your usage and citations, and an interactive bibliography. Users have the ability to review articles and provide their knowledge and insight within the community.

Collection : 74,560,631

Other Services : None

Advanced Search Options : Content type, source, author, journal, discipline

3. Directory of Open Access Journals

A multidisciplinary, community-curated directory, the Directory of Open Access Journals (DOAJ) gives researchers access to high-quality peer-reviewed journals. It has archived more than two million articles from 17,193 journals, allowing you to either browse by subject or search by keyword.

The site was launched in 2003 with the aim of increasing the visibility of OA scholarly journals online. Content on the site covers subjects from science, to law, to fine arts, and everything in between. DOAJ has a commitment to "increase the visibility, accessibility, reputation, usage and impact of quality, peer-reviewed, OA scholarly research journals globally, regardless of discipline, geography or language."

Information about the journal is available with each search result. Abstracts are also available in a collapsible format directly from the search screen. The scholarly article website is somewhat simple, but it is easy to navigate. There are 16 principles of transparency and best practices in scholarly publishing that clearly outline DOAJ policies and standards.

Collection : 6,817,242

Advanced Search Options : Subject, journal, year

4. Education Resources Information Center

The Education Resources Information Center (ERIC) of the Institution of Education Sciences allows you to search by topic for material related to the field of education. Links lead to other sites, where you may have to purchase the information, but you can search for full-text articles only. You can also search only peer-reviewed sources.

The service primarily indexes journals, gray literature (such as technical reports, white papers, and government documents), and books. All sources of material on ERIC go through a formal review process prior to being indexed. ERIC's selection policy is available as a PDF on their website.

The ERIC website has an extensive FAQ section to address user questions. This includes categories like general questions, peer review, and ERIC content. There are also tips for advanced searches, as well as general guidance on the best way to search the database. ERIC is an excellent database for content specific to education.

Collection : 1,292,897

Advanced Search Options : Boolean

5. arXiv e-Print Archive

The arXiv e-Print Archive is run by Cornell University Library and curated by volunteer moderators, and it now offers OA to more than one million e-prints.

There are advisory committees for all eight subjects available on the database. With a stated commitment to an "emphasis on openness, collaboration, and scholarship," the arXiv e-Print Archive is an excellent STEM resource.

The interface is not as user-friendly as some of the other databases available, and the website hosts a blog to provide news and updates, but it is otherwise a straightforward math and science resource. There are simple and advanced search options, and, in addition to conducting searches for specific topics and articles, users can browse content by subject. The arXiv e-Print Archive clearly states that they do not peer review the e-prints in the database.

Collection : 1,983,891

Good Source of Peer-Reviewed Articles : No

Advanced Search Options : Subject, date, title, author, abstract, DOI

6. Social Science Research Network

The Social Science Research Network (SSRN) is a collection of papers from the social sciences community. It is a highly interdisciplinary platform used to search for scholarly articles related to 67 social science topics. SSRN has a variety of research networks for the various topics available through the free scholarly database.

The site offers more than 700,000 abstracts and more than 600,000 full-text papers. There is not yet a specific option to search for only full-text articles, but, because most of the papers on the site are free access, it's not often that you encounter a paywall. There is currently no option to search for only peer-reviewed articles.

You must become a member to use the services, but registration is free and enables you to interact with other scholars around the world. SSRN is "passionately committed to increasing inclusion, diversity and equity in scholarly research," and they encourage and discuss the use of inclusive language in scholarship whenever possible.

Collection : 1,058,739 abstracts; 915,452 articles

Advanced Search Options : Term, author, date, network

7. Public Library of Science

Public Library of Science (PLOS) is a big player in the world of OA science. Publishing 12 OA journals, the nonprofit organization is committed to facilitating openness in academic research. According to the site, "all PLOS content is at the highest possible level of OA, meaning that scientific articles are immediately and freely available to anyone, anywhere."

PLOS outlines four fundamental goals that guide the organization: break boundaries, empower researchers, redefine quality, and open science. All PLOS journals are peer-reviewed, and all 12 journals uphold rigorous ethical standards for research, publication, and scientific reporting.

PLOS does not offer advanced search options. Content is organized by topic into research communities that users can browse through, in addition to options to search for both articles and journals. The PLOS website also has resources for peer reviewers, including guidance on becoming a reviewer and on how to best participate in the peer review process.

Collection : 12 journals

Advanced Search Options : None

8. OpenDOAR

OpenDOAR, or the Directory of Open Access Repositories, is a comprehensive resource for finding free OA journals and articles. Using Google Custom Search, OpenDOAR combs through OA repositories around the world and returns relevant research in all disciplines.

The repositories it searches through are assessed and categorized by OpenDOAR staff to ensure they meet quality standards. Inclusion criteria for the database include requirements for OA content, global access, and categorically appropriate content, in addition to various other quality assurance measures. OpenDOAR has metadata, data, content, preservation, and submission policies for repositories, in addition to two OA policy statements regarding minimum and optimum recommendations.

This database allows users to browse and search repositories, which can then be selected, and articles and data can be accessed from the repository directly. As a repository database, much of the content on the site is geared toward the support of repositories and OA standards.

Collection : 5,768 repositories

Other Services : OpenDOAR offers a variety of additional services. Given the nature of the platform, services are primarily aimed at repositories and institutions, and there is a marked focus on OA in general. Sherpa services are OA archiving tools for authors and institutions.

They also offer various resources for OA support and compliance regarding standards and policies. The publication router matches publications and publishers with appropriate repositories.

There are also services and resources from JISC for repositories for cost management, discoverability, research impact, and interoperability, including ORCID consortium membership information. Additionally, a repository self-assessment tool is available for members.

Advanced Search Options : Name, organization name, repository type, software name, content type, subject, country, region

9. Bielefeld Academic Search Engine

The Bielefeld Academic Search Engine (BASE) is operated by the Bielefeld University Library in Germany, and it offers more than 240 million documents from more than 8,000 sources. Sixty percent of its content is OA, and you can filter your search accordingly.

BASE has rigorous inclusion requirements for content providers regarding quality and relevance, and they maintain a list of content providers for the sake of transparency, which can be easily found on their website. BASE has a fairly elegant interface. Search results can be organized by author, title, or date.

From the search results, items can be selected and exported, added to favorites, emailed, and searched in Google Scholar. There are basic and advanced search features, with the advanced search offering numerous options for refining search criteria. There is also a feature on the website that saves recent searches without additional steps from the user.

Collection : 276,019,066 documents; 9,286 content providers

Advanced Search Options : Author, subject, year, content provider, language, document type, access, terms of reuse

10. Digital Library of the Commons Repository

Run by Indiana University, the Digital Library of the Commons (DLC) Repository is a multidisciplinary journal repository that allows users to access thousands of free and OA articles from around the world. You can browse by document type, date, author, title, and more or search for keywords relevant to your topic.

DCL also offers the Comprehensive Bibliography of the Commons, an image database, and a keyword thesaurus for enhanced search parameters. The repository includes books, book chapters, conference papers, journal articles, surveys, theses and dissertations, and working papers. DCL advanced search features drop-down menus of search types with built-in Boolean search options.

Searches can be sorted by relevance, title, date, or submission date in ascending or descending order. Abstracts are included in selected search results, with access to full texts available, and citations can be exported from the same page. Additionally, the image database search includes tips for better search results.

Collection : 10,784

Advanced Search Options : Author, date, title, subject, sector, region, conference

11. CIA World Factbook

The CIA World Factbook is a little different from the other resources on this list in that it is not an online journal directory or repository. It is, however, a useful free online research database for academics in a variety of disciplines.

All the information is free to access, and it provides facts about every country in the world, which are organized by category and include information about history, geography, transportation, and much more. The World Factbook can be searched by country or region, and there is also information about the world's oceans.

This site contains resources related to the CIA as an organization rather than being a scientific journal database specifically. The site has a user interface that is easy to navigate. The site also provides a section for updates regarding changes to what information is available and how it is organized, making it easier to interact with the information you are searching for.

Collection : 266 countries

12. Paperity

Paperity boasts its status as the "first multidisciplinary aggregator of OA journals and papers." Their focus is on helping you avoid paywalls while connecting you to authoritative research. In addition to providing readers with easy access to thousands of journals, Paperity seeks to help authors reach their audiences and help journals increase their exposure to boost readership.

Paperity has journal articles for every discipline, and the database offers more than a dozen advanced search options, including the length of the paper and the number of authors. There is even an option to include, exclude, or exclusively search gray papers.

Paperity is available for mobile, with both a mobile site and the Paperity Reader, an app that is available for both Android and Apple users. The database is also available on social media. You can interact with Paperity via Twitter and Facebook, and links to their social media are available on their homepage, including their Twitter feed.

Collection : 8,837,396

Advanced Search Options : Title, abstract, journal title, journal ISSN, publisher, year of publication, number of characters, number of authors, DOI, author, affiliation, language, country, region, continent, gray papers

13. dblp Computer Science Bibliography

The dblp Computer Science Bibliography is an online index of major computer science publications. dblp was founded in 1993, though until 2010 it was a university-specific database at the University of Trier in Germany. It is currently maintained by the Schloss Dagstuhl – Leibniz Center for Informatics.

Although it provides access to both OA articles and those behind a paywall, you can limit your search to only OA articles. The site indexes more than three million publications, making it an invaluable resource in the world of computer science. dblp entries are color-coded based on the type of item.

dblp has an extensive FAQ section, so questions that might arise about topics like the database itself, navigating the website, or the data on dblp, in addition to several other topics, are likely to be answered. The website also hosts a blog and has a section devoted to website statistics.

Collection : 5,884,702

14. EconBiz

EconBiz is a great resource for economic and business studies. A service of the Leibniz Information Centre for Economics, it offers access to full texts online, with the option of searching for OA material only. Their literature search is performed across multiple international databases.

EconBiz has an incredibly useful research skills section, with resources such as Guided Walk, a service to help students and researchers navigate searches, evaluate sources, and correctly cite references; the Research Guide EconDesk, a help desk to answer specific questions and provide advice to aid in literature searches; and the Academic Career Kit for what they refer to as Early Career Researchers.

Other helpful resources include personal literature lists, a calendar of events for relevant calls for papers, conferences, and workshops, and an economics terminology thesaurus to help in finding keywords for searches. To stay up-to-date with EconBiz, you can sign up for their newsletter.

Collection : 1,075,219

Advanced Search Options : Title, subject, author, institution, ISBN/ISSN, journal, publisher, language, OA only

15. BioMed Central

BioMed Central provides OA research from more than 300 peer-reviewed journals. While originally focused on resources related to the physical sciences, math, and engineering, BioMed Central has branched out to include journals that cover a broader range of disciplines, with the aim of providing a single platform that provides OA articles for a variety of research needs. You can browse these journals by subject or title, or you can search all articles for your required keyword.

BioMed Central has a commitment to peer-reviewed sources and to the peer review process itself, continually seeking to help and improve the peer review process. They're "committed to maintaining high standards through full and stringent peer review."

Additionally, the website includes resources to assist and support editors as part of their commitment to providing high-quality, peer-reviewed OA articles.

Collection : 507,212

Other Services : BMC administers the International Standard Randomised Controlled Trial Number (ISRCTN) registry. While initially designed for registering clinical trials, since its creation in 2000, the registry has broadened its scope to include other health studies as well.

The registry is recognized by the International Committee of Medical Journal Editors, as well as the World Health Organization (WHO), and it meets the requirements established by the WHO International Clinical Trials Registry Platform.

The study records included in the registry are all searchable and free to access. The ISRCTN registry "supports transparency in clinical research, helps reduce selective reporting of results and ensures an unbiased and complete evidence base."

Advanced Search Options : Author, title, journal, list

A multidisciplinary search engine, JURN provides links to various scholarly websites, articles, and journals that are free to access or OA. Covering the fields of the arts, humanities, business, law, nature, science, and medicine, JURN has indexed almost 5,000 repositories to help you find exactly what you're looking for.

Search features are enhanced by Google, but searches are filtered through their index of repositories. JURN seeks to reach a wide audience, with their search engine tailored to researchers from "university lecturers and students seeking a strong search tool for OA content" and "advanced and ambitious students, age 14-18" to "amateur historians and biographers" and "unemployed and retired lecturers."

That being said, JURN is very upfront about its limitations. They admit to not being a good resource for educational studies, social studies, or psychology, and conference archives are generally not included due to frequently unstable URLs.

Collection : 5,064 indexed journals

Other Services : JURN has a browser add-on called UserScript. This add-on allows users to integrate the JURN database directly into Google Search. When performing a search through Google, the add-on creates a link that sends the search directly to JURN CSE. JURN CSE is a search service that is hosted by Google.

Clicking the link from the Google Search bar will run your search through the JURN database from the Google homepage. There is also an interface for a DuckDuckGo search box; while this search engine has an emphasis on user privacy, for smaller sites that may be indexed by JURN, DuckDuckGo may not provide the same depth of results.

Advanced Search Options : Google search modifiers

Dryad is a digital repository of curated, OA scientific research data. Launched in 2009, it is run by a not-for-profit membership organization, with a community of institutional and publisher members for whom their services have been designed. Members include institutions such as Stanford, UCLA, and Yale, as well as publishers like Oxford University Press and Wiley.

Dryad aims to "promote a world where research data is openly available, integrated with the scholarly literature, and routinely reused to create knowledge." It is free to access for the search and discovery of data. Their user experience is geared toward easy self-depositing, supports Creative Commons licensing, and provides DOIs for all their content.

Note that there is a publishing charge associated if you wish to publish your data in Dryad. When searching datasets, they are accompanied by author information and abstracts for the associated studies, and citation information is provided for easy attribution.

Collection : 44,458

Advanced Search Options : No

Run by the British Library, the E-Theses Online Service (EThOS) allows you to search over 500,000 doctoral theses in a variety of disciplines. All of the doctoral theses available on EThOS have been awarded by higher education institutions in the United Kingdom.

Although some full texts are behind paywalls, you can limit your search to items available for immediate download, either directly through EThOS or through an institution's website. More than half of the records in the database provide access to full-text theses.

EThOS notes that they do not hold all records for all institutions, but they strive to index as many doctoral theses as possible, and the database is constantly expanding, with approximately 3,000 new records added and 2,000 new full-text theses available every month. The availability of full-text theses is dependent on multiple factors, including their availability in the institutional repository and the level of repository development.

Collection : 500,000+

Advanced Search Options : Abstract, author's first name, author's last name, awarding body, current institution, EThOS ID, year, language, qualifications, research supervisor, sponsor/funder, keyword, title

PubMed is a research platform well-known in the fields of science and medicine. It was created and developed by the National Center for Biotechnology Information (NCBI) at the National Library of Medicine (NLM). It has been available since 1996 and offers access to "more than 33 million citations for biomedical literature from MEDLINE, life science journals, and online books."

While PubMed does not provide full-text articles directly, and many full-text articles may be behind paywalls or require subscriptions to access them, when articles are available from free sources, such as through PubMed Central (PMC), those links are provided with the citations and abstracts that PubMed does provide.

PMC, which was established in 2000 by the NLM, is a free full-text archive that includes more than 6,000,000 records. PubMed records link directly to corresponding PMC results. PMC content is provided by publishers and other content owners, digitization projects, and authors directly.

Collection : 33,000,000+

Advanced Search Options : Author's first name, author's last name, identifier, corporation, date completed, date created, date entered, date modified, date published, MeSH, book, conflict of interest statement, EC/RN number, editor, filter, grant number, page number, pharmacological action, volume, publication type, publisher, secondary source ID, text, title, abstract, transliterated title

20. Semantic Scholar

A unique and easy-to-use resource, Semantic Scholar defines itself not just as a research database but also as a "search and discovery tool." Semantic Scholar harnesses the power of artificial intelligence to efficiently sort through millions of science-related papers based on your search terms.

Through this singular application of machine learning, Semantic Scholar expands search results to include topic overviews based on your search terms, with the option to create an alert for or further explore the topic. It also provides links to related topics.

In addition, search results produce "TLDR" summaries in order to provide concise overviews of articles and enhance your research by helping you to navigate quickly and easily through the available literature to find the most relevant information. According to the site, although some articles are behind paywalls, "the data [they] have for those articles is limited," so you can expect to receive mostly full-text results.

Collection : 203,379,033

Other Services : Semantic Scholar supports multiple popular browsers. Content can be accessed through both mobile and desktop versions of Firefox, Microsoft Edge, Google Chrome, Apple Safari, and Opera.

Additionally, Semantic Scholar provides browser extensions for both Chrome and Firefox, so AI-powered scholarly search results are never more than a click away. The mobile interface includes an option for Semantic Swipe, a new way of interacting with your research results.

There are also beta features that can be accessed as part of the Beta Program, which will provide you with features that are being actively developed and require user feedback for further improvement.

Advanced Search Options : Field of study, date range, publication type, author, journal, conference, PDF

Zenodo, powered by the European Organization for Nuclear Research (CERN), was launched in 2013. Taking its name from Zenodotus, the first librarian of the ancient library of Alexandria, Zenodo is a tool "built and developed by researchers, to ensure that everyone can join in open science." Zenodo accepts all research from every discipline in any file format.

However, Zenodo also curates uploads and promotes peer-reviewed material that is available through OA. A DOI is assigned to everything that is uploaded to Zenodo, making research easily findable and citable. You can sort by keyword, title, journal, and more and download OA documents directly from the site.

While there are closed access and restricted access items in the database, the vast majority of research is OA material. Search results can be filtered by access type, making it easy to view the free articles available in the database.

Collection : 2,220,000+

Advanced Search Options : Access, file type, keywords

Check out our roundup of free research databases as a handy one-page PDF.

How to find peer-reviewed articles.

There are a lot of free scholarly articles available from various sources. The internet is a big place. So how do you go about finding peer-reviewed articles when conducting your research? It's important to make sure you are using reputable sources.

The first source of the article is the person or people who wrote it. Checking out the author can give you some initial insight into how much you can trust what you’re reading. Looking into the publication information of your sources can also indicate whether the article is reliable.

Aspects of the article, such as subject and audience, tone, and format, are other things you can look at when evaluating whether the article you're using is valid, reputable, peer-reviewed material. So, let's break that down into various components so you can assess your research to ensure that you're using quality articles and conducting solid research.

Check the Author

Peer-reviewed articles are written by experts or scholars with experience in the field or discipline they're writing about. The research in a peer-reviewed article has to pass a rigorous evaluation process, so it's a foregone conclusion that the author(s) of a peer-reviewed article should have experience or training related to that research.

When evaluating an article, take a look at the author's information. What credentials does the author have to indicate that their research has scholarly weight behind it? Finding out what type of degree the author has—and what that degree is in—can provide insight into what kind of authority the author is on the subject.

Something else that might lend credence to the author's scholarly role is their professional affiliation. A look at what organization or institution they are affiliated with can tell you a lot about their experience or expertise. Where were they trained, and who is verifying their research?

Identify Subject and Audience

The ultimate goal of a study is to answer a question. Scholarly articles are also written for scholarly audiences, especially articles that have gone through the peer review process. This means that the author is trying to reach experts, researchers, academics, and students in the field or topic the research is based on.

Think about the question the author is trying to answer by conducting this research, why, and for whom. What is the subject of the article? What question has it set out to answer? What is the purpose of finding the information? Is the purpose of the article of importance to other scholars? Is it original content?

Research should also be approached analytically. Is the methodology sound? Is the author using an analytical approach to evaluate the data that they have obtained? Are the conclusions they've reached substantiated by their data and analysis? Answering these questions can reveal a lot about the article's validity.

Format Matters

Reliable articles from peer-reviewed sources have certain format elements to be aware of. The first is an abstract. An abstract is a short summary or overview of the article. Does the article have an abstract? It's unlikely that you're reading a peer-reviewed article if it doesn't. Peer-reviewed journals will also have a word count range. If an article seems far too short or incredibly long, that may be reason to doubt it.

Another feature of reliable articles is the sections the information is divided into. Peer-reviewed research articles will have clear, concise sections that appropriately organize the information. This might include a literature review, methodology, results (in the case of research articles), and a conclusion.

One of the most important sections is the references or bibliography. This is where the researcher lists all the sources of their information. A peer-reviewed source will have a comprehensive reference section.

An article that has been written to reach an academic community will have an academic tone. The language that is used, and the way this language is used, is important to consider. If the article is riddled with grammatical errors, confusing syntax, and casual language, it almost definitely didn't make it through the peer review process.

Also consider the use of terminology. Every discipline is going to have standard terminology or jargon that can be used and understood by other academics in the discipline. The language in a peer-reviewed article is going to reflect that.

If the author is going out of their way to explain simple terms, or terms that are standard to the field or discipline, it's unlikely that the article has been peer reviewed, as this is something that the author would be asked to address during the review process.

Publication

The source of the article will be a very good indicator of the likelihood that it was peer reviewed. Where was the article published? Was it published alongside other academic articles in the same discipline? Is it a legitimate and reputable scholarly publication?

A trade publication or newspaper might be legitimate or reputable, but it is not a scholarly source, and it will not have been subject to the peer review process. Scholarly journals are the best resource for peer-reviewed articles, but it's important to remember that not all scholarly journals are peer reviewed.

It's helpful to look at a scholarly source's website, as peer-reviewed journals will have a clear indication of the peer review process. University libraries, institutional repositories, and reliable databases (and now you have a list of legit ones) can also help provide insight into whether an article comes from a peer-reviewed journal.

Common Research Mistakes to Avoid

Research is a lot of work. Even with high standards and good intentions, it's easy to make mistakes. Perhaps you searched for access to scientific journals for free and found the perfect peer-reviewed sources, but you forgot to document everything, and your references are a mess. Or, you only searched for free online articles and missed out on a ground-breaking study that was behind a paywall.

Whether your research is for a degree or to get published or to satisfy your own inquisitive nature, or all of the above, you want all that work to produce quality results. You want your research to be thorough and accurate.

To have any hope of contributing to the literature on your research topic, your results need to be high quality. You might not be able to avoid every potential mistake, but here are some that are both common and easy to avoid.

Sticking to One Source

One of the hallmarks of good research is a healthy reference section. Using a variety of sources gives you a better answer to your question. Even if all of the literature is in agreement, looking at various aspects of the topic may provide you with an entirely different picture than you would have if you looked at your research question from only one angle.

Not Documenting Every Fact

As you conduct your research, do yourself a favor and write everything down. Everything you include in your paper or article that you got from another source is going to need to be added to your references and cited.

It's important, especially if your aim is to conduct ethical, high-quality research, that all of your research has proper attribution. If you don't document as you go, you could end up making a lot of work for yourself if the information you don't write down is something that later, as you write your paper, you really need.

Using Outdated Materials

Academia is an ever-changing landscape. What was true in your academic discipline or area of research ten years ago may have since been disproven. If fifteen studies have come out since the article that you're using was published, it's more than a little likely that you're going to be basing your research on flawed or dated information.

If the information you're basing your research on isn't as up-to-date as possible, your research won't be of quality or able to stand up to any amount of scrutiny. You don't want all of your hard work to be for naught.

Relying Solely on Open Access Journals

OA is a great resource for conducting academic research. There are high-quality journal articles available through OA, and that can be very helpful for your research. But, just because you have access to free articles, that doesn't mean that there's nothing to be found behind a paywall.

Just as dismissing high-quality peer-reviewed articles because they are OA would be limiting, not exploring any paid content at all is equally short-sighted. If you're seeking to conduct thorough and comprehensive research, exploring all of your options for quality sources is going to be to your benefit.

Digging Too Deep or Not Deep Enough

Research is an art form, and it involves a delicate balance of information. If you conduct your research using only broad search terms, you won't be able to answer your research question well, or you'll find that your research provides information that is closely related to your topic but, ultimately, your findings are vague and unsubstantiated.

On the other hand, if you delve deeply into your research topic with specific searches and turn up too many sources, you might have a lot of information that is adjacent to your topic but without focus and perhaps not entirely relevant. It's important to answer your research question concisely but thoroughly.

Different Types of Scholarly Articles

Different types of scholarly articles have different purposes. An original research article, also called an empirical article, is the product of a study or an experiment. This type of article seeks to answer a question or fill a gap in the existing literature.

Research articles will have a methodology, results, and a discussion of the findings of the experiment or research and typically a conclusion.

Review articles overview the current literature and research and provide a summary of what the existing research indicates or has concluded. This type of study will have a section for the literature review, as well as a discussion of the findings of that review. Review articles will have a particularly extensive reference or bibliography section.

Theoretical articles draw on existing literature to create new theories or conclusions, or look at current theories from a different perspective, to contribute to the foundational knowledge of the field of study.

10 Tips for Navigating Journal Databases

Use the right academic journal database for your search, be that interdisciplinary or specific to your field. Or both!

If it's an option, set the search results to return only peer-reviewed sources.

Start by using search terms that are relevant to your topic without being overly specific.

Try synonyms, especially if your keywords aren't returning the desired results.

Even if you've found some good articles, try searching using different terms.

Explore the advanced search features of the database(s).

Learn to use Booleans (AND, OR, NOT) to expand or narrow your results.

Once you've gotten some good results from a more general search, try narrowing your search.

Read through abstracts when trying to find articles relevant to your research.

Keep track of your research and use citation tools. It'll make life easier when it comes time to compile your references.

7 Frequently Asked Questions

1. how do i get articles for free.

Free articles can be found through free online academic journals, OA databases, or other databases that include OA journals and articles. These resources allow you to access free papers online so you can conduct your research without getting stuck behind a paywall.

Academics don't receive payment for the articles they contribute to journals. There are often, in fact, publication fees that scholars pay in order to publish. This is one of the funding structures that allows OA journals to provide free content so that you don't have to pay fees or subscription costs to access journal articles.

2. How Do I Find Journal Articles?

Journal articles can be found in databases and institutional repositories that can be accessed at university libraries. However, online research databases that contain OA articles are the best resource for getting free access to journal articles that are available online.

Peer-reviewed journal articles are the best to use for academic research, and there are a number of databases where you can find peer-reviewed OA journal articles. Once you've found a useful article, you can look through the references for the articles the author used to conduct their research, and you can then search online databases for those articles, too.

3. How Do I Find Peer-Reviewed Articles?

Peer-reviewed articles can be found in reputable scholarly peer-reviewed journals. High-quality journals and journal articles can be found online using academic search engines and free research databases. These resources are excellent for finding OA articles, including peer-reviewed articles.

OA articles are articles that can be accessed for free. While some scholarly search engines and databases include articles that aren't peer reviewed, there are also some that provide only peer-reviewed articles, and databases that include non-peer-reviewed articles often have advanced search features that enable you to select "peer review only." The database will return results that are exclusively peer-reviewed content.

4. What Are Research Databases?

A research database is a list of journals, articles, datasets, and/or abstracts that allows you to easily search for scholarly and academic resources and conduct research online. There are databases that are interdisciplinary and cover a variety of topics.

For example, Paperity might be a great resource for a chemist as well as a linguist, and there are databases that are more specific to a certain field. So, while ERIC might be one of the best educational databases available for OA content, it's not going to be one of the best databases for finding research in the field of microbiology.

5. How Do I Find Scholarly Articles for Specific Fields?

There are interdisciplinary research databases that provide articles in a variety of fields, as well as research databases that provide articles that cater to specific disciplines. Additionally, a journal repository or index can be a helpful resource for finding articles in a specific field.

When searching an interdisciplinary database, there are frequently advanced search features that allow you to narrow the search results down so that they are specific to your field. Selecting "psychology" in the advanced search features will return psychology journal articles in your search results. You can also try databases that are specific to your field.

If you're searching for law journal articles, many law reviews are OA. If you don't know of any databases specific to history, visiting a journal repository or index and searching "history academic journals" can return a list of journals specific to history and provide you with a place to begin your research.

6. Are Peer-Reviewed Articles Really More Legitimate?

The short answer is yes, peer-reviewed articles are more legitimate resources for academic research. The peer review process provides legitimacy, as it is a rigorous review of the content of an article that is performed by scholars and academics who are experts in their field of study. The review provides an evaluation of the quality and credibility of the article.

Non-peer-reviewed articles are not subject to a review process and do not undergo the same level of scrutiny. This means that non-peer-reviewed articles are unlikely, or at least not as likely, to meet the same standards that peer-reviewed articles do.

7. Are Free Article Directories Legitimate?

Yes! As with anything, some databases are going to be better for certain requirements than others. But, a scholarly article database being free is not a reason in itself to question its legitimacy.

Free scholarly article databases can provide access to abstracts, scholarly article websites, journal repositories, and high-quality peer-reviewed journal articles. The internet has a lot of information, and it's often challenging to figure out what information is reliable.

Research databases and article directories are great resources to help you conduct your research. Our list of the best research paper websites is sure to provide you with sources that are totally legit.

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Best Websites To Download Research Papers For Free: Beyond Sci-Hub

Navigating the vast ocean of academic research can be daunting, especially when you’re on a quest for specific research papers without the constraints of paywalls. Fortunately, the digital age has ushered in an era of accessible knowledge, with various platforms offering free downloads of scholarly articles.

In this article, we explore some of the best websites that provide researchers, students, and academicians with free access to a plethora of research papers across diverse fields, ensuring that knowledge remains within everyone’s reach.

Best Websites To Download Research Papers For Free

Google scholar.

As a researcher, you might find Google Scholar to be a repository brimming with academic papers covering a broad span of domains like social sciences, computer science, and humanity, including:

Journal articles
Conference papers, and

Unlike other websites to download research papers, Google Scholar provides free access to a vast collection of scholarly literature, making it one of the best websites to download research.

Not every article is available in full PDF format directly; however, Google Scholar often links to other open access resources like DOAJ (Directory of Open Access Journals) and open-access repositories where you can directly download papers.

For instance, if you’re searching for a specific 2023 research paper in mathematics, you can use Google Scholar to locate the paper and check if it’s available for free download either on the platform itself or through links to various open access sources.

In many cases, Google Scholar integrates with tools like Unpaywall and Open Access Button, which are browser extensions that help you find free versions of paywalled articles.

These extensions often redirect you to open-access content, including those on platforms like Sci-Hub and Library Genesis, although it’s crucial to be aware of the legal and ethical implications of using such services.

ResearchGate

ResearchGate is a unique platform that blends social networking with academic research, making it an essential tool for researchers and scientists across various disciplines.

Here, you have access to a digital library of millions of research papers, spanning fields from computer science to social sciences and beyond.

When you’re on ResearchGate, downloading a research paper is relatively straightforward, especially if it’s open access. Many researchers upload the full PDF of their work, providing free access to their peer-reviewed articles.

If the research paper you’re interested in isn’t available for direct download, ResearchGate offers a unique feature: you can request a copy directly from the author.

This approach not only gets you the paper but also potentially opens a line of communication with leading experts in your field.

It’s important to note that ResearchGate isn’t just a repository; it’s a platform for discovery and connection. You can:

Follow specific researchers
Join discussions, and
Receive notifications about new research in your domain.

While it doesn’t have the controversial direct download links like Sci-Hub or Library Genesis, ResearchGate offers a more ethical and legal route to accessing academic papers.

ScienceOpen

ScienceOpen is a comprehensive repository that hosts a multitude of open-access research articles across various fields, from the social sciences to computer science.

The process of downloading a research paper on ScienceOpen is remarkably straightforward. Since it’s an open-access platform, most of the papers are available to download as PDFs without any cost.

This means you can access high-quality, peer-reviewed academic research without encountering paywalls that are often a barrier in many other scientific platforms.

For instance, if you’re delving into the latest 2023 scientific papers in mathematics, ScienceOpen can be your go-to source. You can easily search for research papers using:

Browsing through various open access journals featured on the site.

The direct download feature simplifies access to these papers, making it convenient for you to obtain the research you need.

Directory of Open Access Journals (DOAJ)

The Directory of Open Access Journals (DOAJ) is a digital library is an extensive repository of open-access, peer-reviewed journals, covering a wide array of subjects from humanities to nuclear science.

When you’re navigating DOAJ, you’ll discover that it’s not just a platform to download research papers; it’s a gateway to a world of academic research.

Each journal article listed is freely accessible, meaning you can download these scholarly articles without any cost or subscription.

The process is simple: search for research papers using specific keywords, subjects, or even DOAJ’s advanced search functionality that includes filters like:

Language, or
The year of publication.

For example, if you’re delving into the latest developments in scientific research in 2023, DOAJ allows you to refine your search to the most recent publications.

Once you find a relevant research paper, you can easily access the full text in PDF format through a direct download link. This is particularly useful for accessing high-quality, open-access research papers that are not always readily available on other platforms like Sci-Hub or Library Genesis.

PubMed hosts millions of research articles, primarily in the fields of medicine and life sciences, but also encompassing a broad range of scientific research.

When you’re on PubMed, you can search for research papers using:

Authors, or
Specific journal names.

While PubMed lists both open-access and subscription-based journal articles, it offers a unique feature for accessing papers for free.

If you’re looking for a particular research paper, say in the domain of computer science or social sciences from 2023, you can directly access its abstract on PubMed. For open access articles, a free full-text link is often available, allowing you to download the research paper in PDF format.

PubMed integrates with tools like Unpaywall and the Open Access Button. These browser extensions help you find open-access versions of the articles you’re interested in, bypassing the paywalls that often restrict access to scholarly literature.

While PubMed itself doesn’t provide direct download links for all articles, its connection with these tools and various open access repositories ensures that you, as a researcher, have greater access to scientific papers.

Sci-Hub (with Caution)

Sci-Hub, often dubbed the ‘Pirate Bay of Science,’ has been a game-changer in the scientific community since its inception by Alexandra Elbakyan in 2011.

It operates as a controversial, yet widely used platform providing free access to millions of research papers and academic articles that are typically locked behind paywalls.

As a researcher, you might find Sci-Hub an intriguing, albeit contentious, tool for accessing scholarly literature.

When you’re looking to download a research paper from Sci-Hub, the process is relatively straightforward. Say you need a journal article on computer science or a groundbreaking study in social sciences from 2023; you just need to have the DOI (Digital Object Identifier) of the paper.

By entering this DOI into Sci-Hub’s search bar, the website bypasses publisher paywalls, offering you direct download links to PDF versions of the articles.

It’s crucial to note that while Sci-Hub provides access to a vast repository of scientific research, its legality is under constant scrutiny. The platform operates via various proxy links and has been the subject of numerous legal battles with publishers and academic institutions.

Nevertheless, Sci-Hub remains a popular go-to for researchers and scientists globally, especially those without access to university libraries or digital archives.

While it opens doors to a wealth of knowledge, users should be aware of the ethical and legal implications of using such a service in their respective countries.

Wrapping Up: You Can Get Free Academic Papers

The digital landscape offers a wealth of resources for accessing academic research without financial barriers. The platforms we share here provide an invaluable service to the scholarly community, democratising access to knowledge and fostering intellectual growth.

Whether you’re a seasoned researcher or a curious student, these websites bridge the gap between you and the vast world of academic literature, ensuring that the pursuit of knowledge remains an inclusive and equitable journey for all. Remember to consider the legal and ethical aspects when using these resources.

Dr Andrew Stapleton has a Masters and PhD in Chemistry from the UK and Australia. He has many years of research experience and has worked as a Postdoctoral Fellow and Associate at a number of Universities. Although having secured funding for his own research, he left academia to help others with his YouTube channel all about the inner workings of academia and how to make it work for you.

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SpringerOpen

The SpringerOpen portfolio has grown tremendously since its launch in 2010, so that we now offer researchers from all areas of science, technology, medicine, the humanities and social sciences a place to publish open access in journals. Publishing with SpringerOpen makes your work freely available online for everyone, immediately upon publication, and our high-level peer-review and production processes guarantee the quality and reliability of the work. Open access books are published by our Springer imprint.

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The top list of academic search engines

1. Google Scholar

4. science.gov, 5. semantic scholar, 6. baidu scholar, get the most out of academic search engines, frequently asked questions about academic search engines, related articles.

Academic search engines have become the number one resource to turn to in order to find research papers and other scholarly sources. While classic academic databases like Web of Science and Scopus are locked behind paywalls, Google Scholar and others can be accessed free of charge. In order to help you get your research done fast, we have compiled the top list of free academic search engines.

Google Scholar is the clear number one when it comes to academic search engines. It's the power of Google searches applied to research papers and patents. It not only lets you find research papers for all academic disciplines for free but also often provides links to full-text PDF files.

Coverage: approx. 200 million articles
Abstracts: only a snippet of the abstract is available
Related articles: ✔
References: ✔
Cited by: ✔
Links to full text: ✔
Export formats: APA, MLA, Chicago, Harvard, Vancouver, RIS, BibTeX

BASE is hosted at Bielefeld University in Germany. That is also where its name stems from (Bielefeld Academic Search Engine).

Coverage: approx. 136 million articles (contains duplicates)
Abstracts: ✔
Related articles: ✘
References: ✘
Cited by: ✘
Export formats: RIS, BibTeX

Search interface of Bielefeld Academic Search Engine aka BASE

CORE is an academic search engine dedicated to open-access research papers. For each search result, a link to the full-text PDF or full-text web page is provided.

Coverage: approx. 136 million articles
Links to full text: ✔ (all articles in CORE are open access)
Export formats: BibTeX

Search interface of the CORE academic search engine

Science.gov is a fantastic resource as it bundles and offers free access to search results from more than 15 U.S. federal agencies. There is no need anymore to query all those resources separately!

Coverage: approx. 200 million articles and reports
Links to full text: ✔ (available for some databases)
Export formats: APA, MLA, RIS, BibTeX (available for some databases)

Semantic Scholar is the new kid on the block. Its mission is to provide more relevant and impactful search results using AI-powered algorithms that find hidden connections and links between research topics.

Coverage: approx. 40 million articles
Export formats: APA, MLA, Chicago, BibTeX

Although Baidu Scholar's interface is in Chinese, its index contains research papers in English as well as Chinese.

Coverage: no detailed statistics available, approx. 100 million articles
Abstracts: only snippets of the abstract are available
Export formats: APA, MLA, RIS, BibTeX

RefSeek searches more than one billion documents from academic and organizational websites. Its clean interface makes it especially easy to use for students and new researchers.

Coverage: no detailed statistics available, approx. 1 billion documents
Abstracts: only snippets of the article are available
Export formats: not available

Consider using a reference manager like Paperpile to save, organize, and cite your references. Paperpile integrates with Google Scholar and many popular databases, so you can save references and PDFs directly to your library using the Paperpile buttons:

Google Scholar is an academic search engine, and it is the clear number one when it comes to academic search engines. It's the power of Google searches applied to research papers and patents. It not only let's you find research papers for all academic disciplines for free, but also often provides links to full text PDF file.

Semantic Scholar is a free, AI-powered research tool for scientific literature developed at the Allen Institute for AI. Sematic Scholar was publicly released in 2015 and uses advances in natural language processing to provide summaries for scholarly papers.

BASE , as its name suggest is an academic search engine. It is hosted at Bielefeld University in Germany and that's where it name stems from (Bielefeld Academic Search Engine).

CORE is an academic search engine dedicated to open access research papers. For each search result a link to the full text PDF or full text web page is provided.

Science.gov is a fantastic resource as it bundles and offers free access to search results from more than 15 U.S. federal agencies. There is no need any more to query all those resources separately!

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SCAMPS: Synthetics for Camera Measurement of Physiological Signals
OpenOOD: Benchmarking Generalized Out-of-Distribution Detection
A Large Scale Search Dataset for Unbiased Learning to Rank
FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning
Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems
MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge
Robustness Analysis of Video-Language Models Against Visual and Language Perturbations
ComMU: Dataset for Combinatorial Music Generation
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs
TAP-Vid: A Benchmark for Tracking Any Point in a Video
DABS 2.0: Improved Datasets and Algorithms for Universal Self-Supervision
Communicating Natural Programs to Humans and Machines
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability
SkinCon: A skin disease dataset densely annotated by domain experts for fine-grained debugging and analysis
PROSPECT: Labeled Tandem Mass Spectrometry Dataset for Machine Learning in Proteomics
FACT: Learning Governing Abstractions Behind Integer Sequences
Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation
PeRFception: Perception using Radiance Fields
A Greek Parliament Proceedings Dataset for Computational Linguistics and Political Analysis
Addressing Resource Scarcity across Sign Languages with Multilingual Pretraining and Unified-Vocabulary Datasets
TGEA 2.0: A Large-Scale Diagnostically Annotated Dataset with Benchmark Tasks for Text Generation of Pretrained Language Models
BackdoorBench: A Comprehensive Benchmark of Backdoor Learning
3DOS: Towards 3D Open Set Learning - Benchmarking and Understanding Semantic Novelty Detection on Point Clouds
Flare7K: A Phenomenological Nighttime Flare Removal Dataset
DC-BENCH: Dataset Condensation Benchmark
Change Event Dataset for Discovery from Spatio-temporal Remote Sensing Imagery
CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks
Understanding Aesthetics with Language: A Photo Critique Dataset for Aesthetic Assessment
IKEA-Manual: Seeing Shape Assembly Step by Step
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
Ontologue: Declarative Benchmark Construction for Ontological Multi-Label Classification
MBW: Multi-view Bootstrapping in the Wild
Enabling Detailed Action Recognition Evaluation Through Video Dataset Augmentation
Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization
NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies
PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding
FlyView: a bio-informed optical flow truth dataset for visual navigation using panoramic stereo vision
Chartalist: Labeled Graph Datasets for UTXO and Account-based Blockchains
A Multi-Task Benchmark for Korean Legal Language Understanding and Judgement Prediction
APT-36K: A Large-scale Benchmark for Animal Pose Estimation and Tracking
OccGen: Selection of Real-world Multilingual Parallel Data Balanced in Gender within Occupations
GriddlyJS: A Web IDE for Reinforcement Learning
Model Zoos: A Dataset of Diverse Populations of Neural Network Models
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification
Beyond Real-world Benchmark Datasets: An Empirical Study of Node Classification with GNNs
The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games
Breaking Bad: A Dataset for Geometric Fracture and Reassembly
USB: A Unified Semi-supervised Learning Benchmark for Classification
NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search
WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models
BigBio: A Framework for Data-Centric Biomedical Natural Language Processing
MATE: Benchmarking Multi-Agent Reinforcement Learning in Distributed Target Coverage Control
CLEVRER-Humans: Describing Physical and Causal Events the Human Way
HandMeThat: Human-Robot Communication in Physical and Social Environments
OpenSRH: optimizing brain tumor surgery using intraoperative stimulated Raman histology
Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset
BLOX: Macro Neural Architecture Search Benchmark and Algorithms
CEDe: A collection of expert-curated datasets with atom-level entity annotations for Optical Chemical Structure Recognition
M4Singer: A Multi-Style, Multi-Singer and Musical Score Provided Mandarin Singing Corpus
LAION-5B: An open large-scale dataset for training next generation image-text models
MSDS: A Large-Scale Chinese Signature and Token Digit String Dataset for Handwriting Verification
Benchmarking and Analyzing 3D Human Pose and Shape Estimation Beyond Algorithms
ViSioNS: Visual Search in Natural Scenes Benchmark
mRI: Multi-modal 3D Human Pose Estimation Dataset using mmWave, RGB-D, and Inertial Sensors
ActionSense: A Multimodal Dataset and Recording Framework for Human Activities Using Wearable Sensors in a Kitchen Environment
The Dollar Street Dataset: Images Representing the Geographic and Socioeconomic Diversity of the World
FETA: Towards Specializing Foundational Models for Expert Task Applications
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection
Evaluating Out-of-Distribution Performance on Document Image Classifiers
Open High-Resolution Satellite Imagery: The WorldStrat Dataset – With Application to Super-Resolution
OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics
A Benchmark for Compositional Visual Reasoning
xView3-SAR: Detecting Dark Fishing Activity Using Synthetic Aperture Radar Imagery
Learning Long-Term Crop Management Strategies with CyclesGym
ETAB: A Benchmark Suite for Visual Representation Learning in Echocardiography
EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records
GOOD: A Graph Out-of-Distribution Benchmark
Is one annotation enough? - A data-centric image classification benchmark for noisy and ambiguous label estimation
MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction
CAESAR: An Embodied Simulator for Generating Multimodal Referring Expression Datasets
JAHS-Bench-201: A Foundation For Research On Joint Architecture And Hyperparameter Search
A Dataset for Efforts Towards Achieving the Sustainable Development Goal of Safe Working Environments
Forecasting Future World Events With Neural Networks
TwiBot-22: Towards Graph-Based Twitter Bot Detection
Avalon: A Benchmark for RL Generalization Using Procedurally Generated Worlds
Long Range Graph Benchmark
Geoclidean: Few-Shot Generalization in Euclidean Geometry
CARLANE: A Lane Detection Benchmark for Unsupervised Domain Adaptation from Simulation to multiple Real-World Domains
EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine
How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning?
OpenFilter: A Framework to Democratize Research Access to Social Media AR Filters
Why do tree-based models still outperform deep learning on typical tabular data?
Multi-LexSum: Real-world Summaries of Civil Rights Lawsuits at Multiple Granularities
Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training Benchmark
Robustness Disparities in Face Detection
AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation
TaiSu: A 166M Large-scale High-Quality Dataset for Chinese Vision-Language Pre-training
Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time
PDEBench: An Extensive Benchmark for Scientific Machine Learning
LIPS - Learning Industrial Physical Simulation benchmark suite
Towards Video Text Visual Question Answering: Benchmark and Baseline
SoundSpaces 2.0: A Simulation Platform for Visual-Acoustic Learning
NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning
NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks
OpenFWI: Large-scale Multi-structural Benchmark Datasets for Full Waveform Inversion
METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets
DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detection
ConfLab: A Data Collection Concept, Dataset, and Benchmark for Machine Analysis of Free-Standing Social Interactions in the Wild
HAPI: A Large-scale Longitudinal Dataset of Commercial ML API Predictions
TempEL: Linking Dynamically Evolving and Newly Emerging Entities
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models
A Survey and Datasheet Repository of Publicly Available US Criminal Justice Datasets
Myriad: a real-world testbed to bridge trajectory optimization and deep learning
TweetNERD - End to End Entity Linking Benchmark for Tweets
AutoWS-Bench-101: Benchmarking Automated Weak Supervision with 100 Labels
SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles
This is the way: designing and compiling LEPISZCZE, a comprehensive NLP benchmark for Polish
A Unified Evaluation of Textual Backdoor Learning: Frameworks and Benchmarks
Kantorovich Strikes Back! Wasserstein GANs are not Optimal Transport?
DART: Articulated Hand Model with Diverse Accessories and Rich Textures
Active-Passive SimStereo - Benchmarking the Cross-Generalization Capabilities of Deep Learning-based Stereo Methods
CGLB: Benchmark Tasks for Continual Graph Learning
ADBench: Anomaly Detection Benchmark
A new dataset for multilingual keyphrase generation
Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systems
DDXPlus: A New Dataset For Automatic Medical Diagnosis
Video compression dataset and benchmark of learning-based video-quality metrics
Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning
MVP-N: A Dataset and Benchmark for Real-World Multi-View Object Classification
pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning
Dungeons and Data: A Large-Scale NetHack Dataset
OpenXAI: Towards a Transparent Evaluation of Model Explanations
Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learning
ENS-10: A Dataset For Post-Processing Ensemble Weather Forecasts
AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier–Stokes Solutions
EPIC-KITCHENS VISOR Benchmark: VIdeo Segmentations and Object Relations
Multilingual Abusive Comment Detection at Scale for Indic Languages
MoCapAct: A Multi-Task Dataset for Simulated Humanoid Control
FLAIR: Federated Learning Annotated Image Repository
StrokeRehab: A Benchmark Dataset for Sub-second Action Identification
Training Uncertainty-Aware Classifiers with Conformalized Deep Learning
Optimizing Relevance Maps of Vision Transformers Improves Robustness
Quantum Speedups of Optimizing Approximately Convex Functions with Applications to Logarithmic Regret Stochastic Convex Bandits
Low-rank lottery tickets: finding efficient low-rank neural networks via matrix differential equations
Towards Improving Faithfulness in Abstractive Summarization
SIREN: Shaping Representations for Detecting Out-of-Distribution Objects
Implicit Neural Representations with Levels-of-Experts
Uplifting Bandits
Infinite-Fidelity Coregionalization for Physical Simulation
RSA: Reducing Semantic Shift from Aggressive Augmentations for Self-supervised Learning
On the Effective Number of Linear Regions in Shallow Univariate ReLU Networks: Convergence Guarantees and Implicit Bias
On Infinite Separations Between Simple and Optimal Mechanisms
TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction
Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation
Automatic differentiation of nonsmooth iterative algorithms
Efficient coding, channel capacity, and the emergence of retinal mosaics
Decentralized Local Stochastic Extra-Gradient for Variational Inequalities
Toward Equation of Motion for Deep Neural Networks: Continuous-time Gradient Descent and Discretization Error Analysis
Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning
Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering
Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret
Counterfactual Neural Temporal Point Process for Estimating Causal Influence of Misinformation on Social Media
Globally Gated Deep Linear Networks
Graph Scattering beyond Wavelet Shackles
Aligning individual brains with fused unbalanced Gromov Wasserstein
SoftPatch: Unsupervised Anomaly Detection with Noisy Data
Kernel Interpolation with Sparse Grids
Ask4Help: Learning to Leverage an Expert for Embodied Tasks
TUSK: Task-Agnostic Unsupervised Keypoints
Concept Activation Regions: A Generalized Framework For Concept-Based Explanations
Matrix Multiplicative Weights Updates in Quantum Zero-Sum Games: Conservation Laws & Recurrence
Posted Pricing and Dynamic Prior-independent Mechanisms with Value Maximizers
Training stochastic stabilized supralinear networks by dynamics-neutral growth
Chefs' Random Tables: Non-Trigonometric Random Features
NeuForm: Adaptive Overfitting for Neural Shape Editing
STaR: Bootstrapping Reasoning With Reasoning
A Causal Analysis of Harm
Network change point localisation under local differential privacy
DISCO: Adversarial Defense with Local Implicit Functions
Does GNN Pretraining Help Molecular Representation?
FedAvg with Fine Tuning: Local Updates Lead to Representation Learning
GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images
Re-Analyze Gauss: Bounds for Private Matrix Approximation via Dyson Brownian Motion
Locating and Editing Factual Associations in GPT
Faster Linear Algebra for Distance Matrices
Causal Inference with Non-IID Data using Linear Graphical Models
Extra-Newton: A First Approach to Noise-Adaptive Accelerated Second-Order Methods
ALMA: Hierarchical Learning for Composite Multi-Agent Tasks
Diversified Recommendations for Agents with Adaptive Preferences
Optimizing Data Collection for Machine Learning
VeriDark: A Large-Scale Benchmark for Authorship Verification on the Dark Web
CoNT: Contrastive Neural Text Generation
Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline
Zeroth-Order Negative Curvature Finding: Escaping Saddle Points without Gradients
Towards Practical Control of Singular Values of Convolutional Layers
Riemannian Neural SDE: Learning Stochastic Representations on Manifolds
Pragmatically Learning from Pedagogical Demonstrations in Multi-Goal Environments
A Contrastive Framework for Neural Text Generation
AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos
Two-Stream Network for Sign Language Recognition and Translation
Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks
Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment
Roadblocks for Temporarily Disabling Shortcuts and Learning New Knowledge
Learning Bipartite Graphs: Heavy Tails and Multiple Components
Vision Transformers provably learn spatial structure
Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and Denoising
Teach Less, Learn More: On the Undistillable Classes in Knowledge Distillation
Hand-Object Interaction Image Generation
Feature Learning in $L_2$-regularized DNNs: Attraction/Repulsion and Sparsity
Bridging the Gap: Unifying the Training and Evaluation of Neural Network Binary Classifiers
On the Discrimination Risk of Mean Aggregation Feature Imputation in Graphs
Efficient and Modular Implicit Differentiation
NeuroSchedule: A Novel Effective GNN-based Scheduling Method for High-level Synthesis
Recursive Reinforcement Learning
Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure
Distribution-Informed Neural Networks for Domain Adaptation Regression
On the Interpretability of Regularisation for Neural Networks Through Model Gradient Similarity
Exploiting Semantic Relations for Glass Surface Detection
Doubly-Asynchronous Value Iteration: Making Value Iteration Asynchronous in Actions
Function Classes for Identifiable Nonlinear Independent Component Analysis
GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models
Recovering Private Text in Federated Learning of Language Models
Contrastive Language-Image Pre-Training with Knowledge Graphs
Fast Mixing of Stochastic Gradient Descent with Normalization and Weight Decay
Disentangling the Predictive Variance of Deep Ensembles through the Neural Tangent Kernel
Quantile Constrained Reinforcement Learning: A Reinforcement Learning Framework Constraining Outage Probability
Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members
Diversity vs. Recognizability: Human-like generalization in one-shot generative models
SketchBoost: Fast Gradient Boosted Decision Tree for Multioutput Problems
DeVRF: Fast Deformable Voxel Radiance Fields for Dynamic Scenes
Emergence of Hierarchical Layers in a Single Sheet of Self-Organizing Spiking Neurons
Adapting to Online Label Shift with Provable Guarantees
Offline Multi-Agent Reinforcement Learning with Knowledge Distillation
Visual correspondence-based explanations improve AI robustness and human-AI team accuracy
Large-Scale Differentiable Causal Discovery of Factor Graphs
Near Instance-Optimal PAC Reinforcement Learning for Deterministic MDPs
A Conditional Randomization Test for Sparse Logistic Regression in High-Dimension
Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential Game
On the SDEs and Scaling Rules for Adaptive Gradient Algorithms
Data Augmentation MCMC for Bayesian Inference from Privatized Data
Dynamic Tensor Product Regression
Introspective Learning : A Two-Stage approach for Inference in Neural Networks
Score-Based Diffusion meets Annealed Importance Sampling
Local Identifiability of Deep ReLU Neural Networks: the Theory
Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning
A Continuous Time Framework for Discrete Denoising Models
Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks
Infinite Recommendation Networks: A Data-Centric Approach
Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
Learning Distributions Generated by Single-Layer ReLU Networks in the Presence of Arbitrary Outliers
RainNet: A Large-Scale Imagery Dataset and Benchmark for Spatial Precipitation Downscaling
VICRegL: Self-Supervised Learning of Local Visual Features
Learning to Find Proofs and Theorems by Learning to Refine Search Strategies: The Case of Loop Invariant Synthesis
Generalization for multiclass classification with overparameterized linear models
Okapi: Generalising Better by Making Statistical Matches Match
Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference
Probabilistic Transformer: Modelling Ambiguities and Distributions for RNA Folding and Molecule Design
Adversarial Reprogramming Revisited
A Near-Optimal Best-of-Both-Worlds Algorithm for Online Learning with Feedback Graphs
Uncertainty Estimation Using Riemannian Model Dynamics for Offline Reinforcement Learning
Left Heavy Tails and the Effectiveness of the Policy and Value Networks in DNN-based best-first search for Sokoban Planning
The Pitfalls of Regularization in Off-Policy TD Learning
OmniVL: One Foundation Model for Image-Language and Video-Language Tasks
CCCP is Frank-Wolfe in disguise
Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding
Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning
Adam Can Converge Without Any Modification On Update Rules
A Consistent and Differentiable Lp Canonical Calibration Error Estimator
Exploration-Guided Reward Shaping for Reinforcement Learning under Sparse Rewards
Detection and Localization of Changes in Conditional Distributions
TransTab: Learning Transferable Tabular Transformers Across Tables
Spatial Mixture-of-Experts
TransBoost: Improving the Best ImageNet Performance using Deep Transduction
A Multilabel Classification Framework for Approximate Nearest Neighbor Search
On Efficient Online Imitation Learning via Classification
Inherently Explainable Reinforcement Learning in Natural Language
Inverse Game Theory for Stackelberg Games: the Blessing of Bounded Rationality
Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards
$k$-Sliced Mutual Information: A Quantitative Study of Scalability with Dimension
A Direct Approximation of AIXI Using Logical State Abstractions
Transition to Linearity of General Neural Networks with Directed Acyclic Graph Architecture
Towards Efficient Post-training Quantization of Pre-trained Language Models
A Unified Analysis of Federated Learning with Arbitrary Client Participation
Self-supervised surround-view depth estimation with volumetric feature fusion
Robust Bayesian Regression via Hard Thresholding
On the Efficient Implementation of High Accuracy Optimality of Profile Maximum Likelihood
The price of unfairness in linear bandits with biased feedback
Cooperative Distribution Alignment via JSD Upper Bound
Censored Quantile Regression Neural Networks for Distribution-Free Survival Analysis
Dataset Inference for Self-Supervised Models
Active Learning Through a Covering Lens
Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization
Wavelet Score-Based Generative Modeling
Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent
The Curse of Unrolling: Rate of Differentiating Through Optimization
ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers
Dual-discriminative Graph Neural Network for Imbalanced Graph-level Anomaly Detection
Improved Convergence Rate of Stochastic Gradient Langevin Dynamics with Variance Reduction and its Application to Optimization
What You See is What You Classify: Black Box Attributions
A Closer Look at Prototype Classifier for Few-shot Image Classification
Graph Reordering for Cache-Efficient Near Neighbor Search
Trade-off between Payoff and Model Rewards in Shapley-Fair Collaborative Machine Learning
Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging
Adaptively Exploiting d-Separators with Causal Bandits
Generative Neural Articulated Radiance Fields
Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos
Identification, Amplification and Measurement: A bridge to Gaussian Differential Privacy
BagFlip: A Certified Defense Against Data Poisoning
On the Convergence Theory for Hessian-Free Bilevel Algorithms
On the Sample Complexity of Stabilizing LTI Systems on a Single Trajectory
Gradient Descent Is Optimal Under Lower Restricted Secant Inequality And Upper Error Bound
Maximum Common Subgraph Guided Graph Retrieval: Late and Early Interaction Networks
Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning
Refining Low-Resource Unsupervised Translation by Language Disentanglement of Multilingual Translation Model
Analyzing Data-Centric Properties for Graph Contrastive Learning
RényiCL: Contrastive Representation Learning with Skew Rényi Divergence
Scalable Neural Video Representations with Learnable Positional Features
Towards Improving Calibration in Object Detection Under Domain Shift
GenSDF: Two-Stage Learning of Generalizable Signed Distance Functions
Approaching Quartic Convergence Rates for Quasi-Stochastic Approximation with Application to Gradient-Free Optimization
Neural Circuit Architectural Priors for Embodied Control
Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP
Understanding Deep Neural Function Approximation in Reinforcement Learning via $\epsilon$-Greedy Exploration
LIFT: Language-Interfaced Fine-Tuning for Non-language Machine Learning Tasks
Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels?
Stochastic Multiple Target Sampling Gradient Descent
If Influence Functions are the Answer, Then What is the Question?
[Re] Replication Study of DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks
A Projection-free Algorithm for Constrained Stochastic Multi-level Composition Optimization
A composable machine-learning approach for steady-state simulations on high-resolution grids
Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging
[Re] Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentation
Amortized Projection Optimization for Sliced Wasserstein Generative Models
Trading Off Resource Budgets For Improved Regret Bounds
Robust On-Policy Sampling for Data-Efficient Policy Evaluation in Reinforcement Learning
Matching in Multi-arm Bandit with Collision
Combinatorial Bandits with Linear Constraints: Beyond Knapsacks and Fairness
Evaluating Graph Generative Models with Contrastively Learned Features
Single-pass Streaming Lower Bounds for Multi-armed Bandits Exploration with Instance-sensitive Sample Complexity
The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm
A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks
Rate-Distortion Theoretic Bounds on Generalization Error for Distributed Learning
One-shot Neural Backdoor Erasing via Adversarial Weight Masking
Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation
Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts
Movement Penalized Bayesian Optimization with Application to Wind Energy Systems
Two-layer neural network on infinite dimensional data: global optimization guarantee in the mean-field regime
Efficient Aggregated Kernel Tests using Incomplete $U$-statistics
Recurrent Memory Transformer
Unsupervised Learning From Incomplete Measurements for Inverse Problems
An empirical analysis of compute-optimal large language model training
DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems
SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning
House of Cans: Covert Transmission of Internal Datasets via Capacity-Aware Neuron Steganography
A Unifying Framework for Online Optimization with Long-Term Constraints
Better Best of Both Worlds Bounds for Bandits with Switching Costs
Renyi Differential Privacy of Propose-Test-Release and Applications to Private and Robust Machine Learning
Earthformer: Exploring Space-Time Transformers for Earth System Forecasting
Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression
Look Around and Refer: 2D Synthetic Semantics Knowledge Distillation for 3D Visual Grounding
Variational inference via Wasserstein gradient flows
Efficient Risk-Averse Reinforcement Learning
Operator Splitting Value Iteration
Composite Feature Selection Using Deep Ensembles
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent
Contrastive Adapters for Foundation Model Group Robustness
Domain Generalization by Learning and Removing Domain-specific Features
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification
Physics-Embedded Neural Networks: Graph Neural PDE Solvers with Mixed Boundary Conditions
SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos
Debiased Self-Training for Semi-Supervised Learning
Learning Recourse on Instance Environment to Enhance Prediction Accuracy
Differentially Private Learning with Margin Guarantees
Provable General Function Class Representation Learning in Multitask Bandits and MDP
Characterization of Excess Risk for Locally Strongly Convex Population Risk
Extrapolative Continuous-time Bayesian Neural Network for Fast Training-free Test-time Adaptation
SNAKE: Shape-aware Neural 3D Keypoint Field
SIXO: Smoothing Inference with Twisted Objectives
Learning Articulated Rigid Body Dynamics with Lagrangian Graph Neural Network
Gradient Descent: The Ultimate Optimizer
Self-Consistent Dynamical Field Theory of Kernel Evolution in Wide Neural Networks
Batch size-invariance for policy optimization
Distributionally robust weighted k-nearest neighbors
On the Importance of Gradient Norm in PAC-Bayesian Bounds
Fair Bayes-Optimal Classifiers Under Predictive Parity
Analyzing Lottery Ticket Hypothesis from PAC-Bayesian Theory Perspective
Counterfactual Fairness with Partially Known Causal Graph
When Privacy Meets Partial Information: A Refined Analysis of Differentially Private Bandits
Efficient identification of informative features in simulation-based inference
Transform Once: Efficient Operator Learning in Frequency Domain
Expansion and Shrinkage of Localization for Weakly-Supervised Semantic Segmentation
Deep Active Learning by Leveraging Training Dynamics
Rate-Optimal Online Convex Optimization in Adaptive Linear Control
SAPipe: Staleness-Aware Pipeline for Data Parallel DNN Training
Understanding Programmatic Weak Supervision via Source-aware Influence Function
Mind Reader: Reconstructing complex images from brain activities
A Neural Corpus Indexer for Document Retrieval
CUP: Critic-Guided Policy Reuse
Low-Rank Modular Reinforcement Learning via Muscle Synergy
RORL: Robust Offline Reinforcement Learning via Conservative Smoothing
Safe Opponent-Exploitation Subgame Refinement
LAPO: Latent-Variable Advantage-Weighted Policy Optimization for Offline Reinforcement Learning
A Primer for Neural Arithmetic Logic Modules
Improving Task-Specific Generalization in Few-Shot Learning via Adaptive Vicinal Risk Minimization
Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers
Look More but Care Less in Video Recognition
Adversarial Task Up-sampling for Meta-learning
Let Images Give You More: Point Cloud Cross-Modal Training for Shape Analysis
Peer Prediction for Learning Agents
Cache-Augmented Inbatch Importance Resampling for Training Recommender Retriever
Interaction Modeling with Multiplex Attention
Learning to Configure Computer Networks with Neural Algorithmic Reasoning
Can Adversarial Training Be Manipulated By Non-Robust Features?
Uncertainty-Aware Hierarchical Refinement for Incremental Implicitly-Refined Classification
MGNNI: Multiscale Graph Neural Networks with Implicit Layers
Discrete Compositional Representations as an Abstraction for Goal Conditioned Reinforcement Learning
MoVQ: Modulating Quantized Vectors for High-Fidelity Image Generation
Learning Mixed Multinomial Logits with Provable Guarantees
Relational Reasoning via Set Transformers: Provable Efficiency and Applications to MARL
Hilbert Distillation for Cross-Dimensionality Networks
Recurrent Video Restoration Transformer with Guided Deformable Attention
Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone
Unified Optimal Transport Framework for Universal Domain Adaptation
Learning Deep Input-Output Stable Dynamics
Batch Bayesian Optimization on Permutations using the Acquisition Weighted Kernel
Neural Topological Ordering for Computation Graphs
Memory Efficient Continual Learning with Transformers
Efficient Knowledge Distillation from Model Checkpoints
EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks
SelecMix: Debiased Learning by Contradicting-pair Sampling
Coordinate Linear Variance Reduction for Generalized Linear Programming
Local Latent Space Bayesian Optimization over Structured Inputs
Memorization and Optimization in Deep Neural Networks with Minimum Over-parameterization
Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure
Learning Robust Dynamics through Variational Sparse Gating
VER: Scaling On-Policy RL Leads to the Emergence of Navigation in Embodied Rearrangement
A Unified Framework for Deep Symbolic Regression
[Re] A Cluster-based Approach for Improving Isotropy in Contextual Embedding Space
Is Sortition Both Representative and Fair?
All Politics is Local: Redistricting via Local Fairness
Learning Interface Conditions in Domain Decomposition Solvers
Off-Policy Evaluation for Action-Dependent Non-stationary Environments
Factored DRO: Factored Distributionally Robust Policies for Contextual Bandits
Causal Discovery in Linear Latent Variable Models Subject to Measurement Error
Human-AI Collaborative Bayesian Optimisation
SNN-RAT: Robustness-enhanced Spiking Neural Network through Regularized Adversarial Training
OOD Link Prediction Generalization Capabilities of Message-Passing GNNs in Larger Test Graphs
Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems
GAR: Generalized Autoregression for Multi-Fidelity Fusion
Learning Representations via a Robust Behavioral Metric for Deep Reinforcement Learning
Environment Diversification with Multi-head Neural Network for Invariant Learning
MetaTeacher: Coordinating Multi-Model Domain Adaptation for Medical Image Classification
Collaborative Learning by Detecting Collaboration Partners
DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detection
Set-based Meta-Interpolation for Few-Task Meta-Learning
Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian Manifold
Error Analysis of Tensor-Train Cross Approximation
Trading off Utility, Informativeness, and Complexity in Emergent Communication
Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights
Learning to Break the Loop: Analyzing and Mitigating Repetitions for Neural Text Generation
A Damped Newton Method Achieves Global $\mathcal O \left(\frac{1}{k^2}\right)$ and Local Quadratic Convergence Rate
Finding Second-Order Stationary Points in Nonconvex-Strongly-Concave Minimax Optimization
Private Set Generation with Discriminative Information
Robust Semi-Supervised Learning when Not All Classes have Labels
Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization
"Lossless" Compression of Deep Neural Networks: A High-dimensional Neural Tangent Kernel Approach
GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networks
Finding and Listing Front-door Adjustment Sets
Bridging the Gap from Asymmetry Tricks to Decorrelation Principles in Non-contrastive Self-supervised Learning
Logical Credal Networks
Sharp Analysis of Stochastic Optimization under Global Kurdyka-Lojasiewicz Inequality
SInGE: Sparsity via Integrated Gradients Estimation of Neuron Relevance
A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian Process Bandits
Rethinking the compositionality of point clouds through regularization in the hyperbolic space
Identifiability of deep generative models without auxiliary information
Sub-exponential time Sum-of-Squares lower bounds for Principal Components Analysis
Robust Anytime Learning of Markov Decision Processes
COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics
Simultaneous Missing Value Imputation and Structure Learning with Groups
Provably Efficient Model-Free Constrained RL with Linear Function Approximation
Private Estimation with Public Data
Friendly Noise against Adversarial Noise: A Powerful Defense against Data Poisoning Attack
Multi-Fidelity Best-Arm Identification
Off-Policy Evaluation with Deficient Support Using Side Information
Challenging Common Assumptions in Convex Reinforcement Learning
Decision Trees with Short Explainable Rules
List-Decodable Sparse Mean Estimation
Stochastic Adaptive Activation Function
Rethinking Knowledge Graph Evaluation Under the Open-World Assumption
A Theoretical Framework for Inference Learning
OPEN: Orthogonal Propagation with Ego-Network Modeling
On the Frequency-bias of Coordinate-MLPs
Generalization Properties of NAS under Activation and Skip Connection Search
Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization)
Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study
A Rotated Hyperbolic Wrapped Normal Distribution for Hierarchical Representation Learning
Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields
A general approximation lower bound in $L^p$ norm, with applications to feed-forward neural networks
CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP
Communication Efficient Distributed Learning for Kernelized Contextual Bandits
Communication Efficient Federated Learning for Generalized Linear Bandits
Versatile Multi-stage Graph Neural Network for Circuit Representation
Cost-Sensitive Self-Training for Optimizing Non-Decomposable Metrics
Understanding Square Loss in Training Overparametrized Neural Network Classifiers
The Gyro-Structure of Some Matrix Manifolds
Multi-view Subspace Clustering on Topological Manifold
HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks
S$^3$-NeRF: Neural Reflectance Field from Shading and Shadow under a Single Viewpoint
PaCo: Parameter-Compositional Multi-task Reinforcement Learning
IALE: Imitating Active Learner Ensembles
Score-based Generative Modeling Secretly Minimizes the Wasserstein Distance
Estimating Noise Transition Matrix with Label Correlations for Noisy Multi-Label Learning
Momentum Aggregation for Private Non-convex ERM
SCL-WC: Cross-Slide Contrastive Learning for Weakly-Supervised Whole-Slide Image Classification
Differentially Private Online-to-batch for Smooth Losses
Distributionally Robust Optimization with Data Geometry
Decentralized Training of Foundation Models in Heterogeneous Environments
On the convergence of policy gradient methods to Nash equilibria in general stochastic games
Sample Complexity of Learning Heuristic Functions for Greedy-Best-First and A* Search
Rank Diminishing in Deep Neural Networks
Don't Pour Cereal into Coffee: Differentiable Temporal Logic for Temporal Action Segmentation
Lethal Dose Conjecture on Data Poisoning
Learning Substructure Invariance for Out-of-Distribution Molecular Representations
NeuPhysics: Editable Neural Geometry and Physics from Monocular Videos
Understanding the Evolution of Linear Regions in Deep Reinforcement Learning
RecursiveMix: Mixed Learning with History
DeepTOP: Deep Threshold-Optimal Policy for MDPs and RMABs
Fairness Reprogramming
S-Prompts Learning with Pre-trained Transformers: An Occam’s Razor for Domain Incremental Learning
Coded Residual Transform for Generalizable Deep Metric Learning
Embodied Scene-aware Human Pose Estimation
Generative Status Estimation and Information Decoupling for Image Rain Removal
Subsidiary Prototype Alignment for Universal Domain Adaptation
Align then Fusion: Generalized Large-scale Multi-view Clustering with Anchor Matching Correspondences
DOMINO: Decomposed Mutual Information Optimization for Generalized Context in Meta-Reinforcement Learning
EcoFormer: Energy-Saving Attention with Linear Complexity
Machine Learning on Graphs: A Model and Comprehensive Taxonomy
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization
Self-Supervised Visual Representation Learning with Semantic Grouping
Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning
Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Models
Active Labeling: Streaming Stochastic Gradients
SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation
Polynomial Neural Fields for Subband Decomposition and Manipulation
Visual Concepts Tokenization
Phase Transition from Clean Training to Adversarial Training
HSurf-Net: Normal Estimation for 3D Point Clouds by Learning Hyper Surfaces
Adversarial Style Augmentation for Domain Generalized Urban-Scene Segmentation
Natural Color Fool: Towards Boosting Black-box Unrestricted Attacks
Cross-Image Context for Single Image Inpainting
TOIST: Task Oriented Instance Segmentation Transformer with Noun-Pronoun Distillation
Is Out-of-Distribution Detection Learnable?
Masked Autoencoders As Spatiotemporal Learners
Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork
PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds
Prototypical VoteNet for Few-Shot 3D Point Cloud Object Detection
Optimistic Tree Searches for Combinatorial Black-Box Optimization
Tensor Wheel Decomposition and Its Tensor Completion Application
PALBERT: Teaching ALBERT to Ponder
Towards Efficient 3D Object Detection with Knowledge Distillation
Towards Lightweight Black-Box Attack Against Deep Neural Networks
HumanLiker: A Human-like Object Detector to Model the Manual Labeling Process
Learn what matters: cross-domain imitation learning with task-relevant embeddings
Whitening Convergence Rate of Coupling-based Normalizing Flows
Hierarchical Normalization for Robust Monocular Depth Estimation
Unsupervised Multi-Object Segmentation by Predicting Probable Motion Patterns
On the Strong Correlation Between Model Invariance and Generalization
Q-ViT: Accurate and Fully Quantized Low-bit Vision Transformer
Fully Sparse 3D Object Detection
Learning Multi-resolution Functional Maps with Spectral Attention for Robust Shape Matching
A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning
Towards Robust Blind Face Restoration with Codebook Lookup Transformer
Improved Fine-Tuning by Better Leveraging Pre-Training Data
TotalSelfScan: Learning Full-body Avatars from Self-Portrait Videos of Faces, Hands, and Bodies
Cross Aggregation Transformer for Image Restoration
Behavior Transformers: Cloning $k$ modes with one stone
What Makes a "Good" Data Augmentation in Knowledge Distillation - A Statistical Perspective
Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection
Discrete-Convex-Analysis-Based Framework for Warm-Starting Algorithms with Predictions
Divert More Attention to Vision-Language Tracking
Trajectory Inference via Mean-field Langevin in Path Space
ElasticMVS: Learning elastic part representation for self-supervised multi-view stereopsis
A2: Efficient Automated Attacker for Boosting Adversarial Training
PerfectDou: Dominating DouDizhu with Perfect Information Distillation
MsSVT: Mixed-scale Sparse Voxel Transformer for 3D Object Detection on Point Clouds
Towards Versatile Embodied Navigation
Product Ranking for Revenue Maximization with Multiple Purchases
Remember the Past: Distilling Datasets into Addressable Memories for Neural Networks
ResT V2: Simpler, Faster and Stronger
In the Eye of the Beholder: Robust Prediction with Causal User Modeling
Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classification
Multi-modal Grouping Network for Weakly-Supervised Audio-Visual Video Parsing
Mining Unseen Classes via Regional Objectness: A Simple Baseline for Incremental Segmentation
Panchromatic and Multispectral Image Fusion via Alternating Reverse Filtering Network
Pay attention to your loss : understanding misconceptions about Lipschitz neural networks
End-to-end Symbolic Regression with Transformers
SPoVT: Semantic-Prototype Variational Transformer for Dense Point Cloud Semantic Completion
Unsupervised Representation Learning from Pre-trained Diffusion Probabilistic Models
What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods
Stochastic Window Transformer for Image Restoration
A Closer Look at Weakly-Supervised Audio-Visual Source Localization
Semi-Discrete Normalizing Flows through Differentiable Tessellation
Blackbox Attacks via Surrogate Ensemble Search
Saliency-Aware Neural Architecture Search
ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation
Learning Best Combination for Efficient N:M Sparsity
Predicting Label Distribution from Multi-label Ranking
Instance-Based Uncertainty Estimation for Gradient-Boosted Regression Trees
Semantic Diffusion Network for Semantic Segmentation
Regret Bounds for Information-Directed Reinforcement Learning
A Spectral Approach to Item Response Theory
UDC: Unified DNAS for Compressible TinyML Models for Neural Processing Units
AutoLink: Self-supervised Learning of Human Skeletons and Object Outlines by Linking Keypoints
Optimistic Mirror Descent Either Converges to Nash or to Strong Coarse Correlated Equilibria in Bimatrix Games
Parameter-Efficient Masking Networks
Learning Distinct and Representative Modes for Image Captioning
Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Images
HUMANISE: Language-conditioned Human Motion Generation in 3D Scenes
VCT: A Video Compression Transformer
Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting
VITA: Video Instance Segmentation via Object Token Association
A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective
Geometry-aware Two-scale PIFu Representation for Human Reconstruction
Causally motivated multi-shortcut identification and removal
SegViT: Semantic Segmentation with Plain Vision Transformers
Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?
Masked Autoencoders that Listen
Semi-Supervised Semantic Segmentation via Gentle Teaching Assistant
Video-based Human-Object Interaction Detection from Tubelet Tokens
Learning Equivariant Segmentation with Instance-Unique Querying
Enhanced Latent Space Blind Model for Real Image Denoising via Alternative Optimization
High-dimensional Additive Gaussian Processes under Monotonicity Constraints
Learning Generalizable Part-based Feature Representation for 3D Point Clouds
Constants of motion network
Asymptotically Unbiased Instance-wise Regularized Partial AUC Optimization: Theory and Algorithm
Rethinking Alignment in Video Super-Resolution Transformers
Robust Testing in High-Dimensional Sparse Models
INRAS: Implicit Neural Representation for Audio Scenes
BMU-MoCo: Bidirectional Momentum Update for Continual Video-Language Modeling
DropCov: A Simple yet Effective Method for Improving Deep Architectures
Antigen-Specific Antibody Design and Optimization with Diffusion-Based Generative Models for Protein Structures
Monocular Dynamic View Synthesis: A Reality Check
A Mixture Of Surprises for Unsupervised Reinforcement Learning
QueryPose: Sparse Multi-Person Pose Regression via Spatial-Aware Part-Level Query
Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning
Misspecified Phase Retrieval with Generative Priors
Watermarking for Out-of-distribution Detection
Error Correction Code Transformer
Maximum Class Separation as Inductive Bias in One Matrix
Sequencer: Deep LSTM for Image Classification
Self-Supervised Learning via Maximum Entropy Coding
Giga-scale Kernel Matrix-Vector Multiplication on GPU
Scalable Infomin Learning
Multi-dataset Training of Transformers for Robust Action Recognition
ZARTS: On Zero-order Optimization for Neural Architecture Search
Online Training Through Time for Spiking Neural Networks
Multi-Instance Causal Representation Learning for Instance Label Prediction and Out-of-Distribution Generalization
P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting
Towards Theoretically Inspired Neural Initialization Optimization
Vision GNN: An Image is Worth Graph of Nodes
Rotation-Equivariant Conditional Spherical Neural Fields for Learning a Natural Illumination Prior
Supported Policy Optimization for Offline Reinforcement Learning
AutoMS: Automatic Model Selection for Novelty Detection with Error Rate Control
Increasing Confidence in Adversarial Robustness Evaluations
Generalization Bounds for Estimating Causal Effects of Continuous Treatments
Non-Markovian Reward Modelling from Trajectory Labels via Interpretable Multiple Instance Learning
Learning Consistency-Aware Unsigned Distance Functions Progressively from Raw Point Clouds
Why Do Artificially Generated Data Help Adversarial Robustness
Learning Infinite-Horizon Average-Reward Restless Multi-Action Bandits via Index Awareness
Theory and Approximate Solvers for Branched Optimal Transport with Multiple Sources
New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma
PointTAD: Multi-Label Temporal Action Detection with Learnable Query Points
On the Generalizability and Predictability of Recommender Systems
Polyhistor: Parameter-Efficient Multi-Task Adaptation for Dense Vision Tasks
Generative Visual Prompt: Unifying Distributional Control of Pre-Trained Generative Models
Stability and Generalization Analysis of Gradient Methods for Shallow Neural Networks
Physically-Based Face Rendering for NIR-VIS Face Recognition
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks
Few-Shot Continual Active Learning by a Robot
MultiScan: Scalable RGBD scanning for 3D environments with articulated objects
Transformer-based Working Memory for Multiagent Reinforcement Learning with Action Parsing
Structural Kernel Search via Bayesian Optimization and Symbolical Optimal Transport
Biologically Inspired Dynamic Thresholds for Spiking Neural Networks
Don't Roll the Dice, Ask Twice: The Two-Query Distortion of Matching Problems and Beyond
A Unified Model for Multi-class Anomaly Detection
A framework for bilevel optimization that enables stochastic and global variance reduction algorithms
SAViT: Structure-Aware Vision Transformer Pruning via Collaborative Optimization
Masked Generative Adversarial Networks are Data-Efficient Generation Learners
Training Spiking Neural Networks with Event-driven Backpropagation
MCMAE: Masked Convolution Meets Masked Autoencoders
Learning Physical Dynamics with Subequivariant Graph Neural Networks
Online PAC-Bayes Learning
Implicit Warping for Animation with Image Sets
Rethinking Resolution in the Context of Efficient Video Recognition
RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning
CEBaB: Estimating the Causal Effects of Real-World Concepts on NLP Model Behavior
Natural gradient enables fast sampling in spiking neural networks
MultiGuard: Provably Robust Multi-label Classification against Adversarial Examples
Efficient and Effective Multi-task Grouping via Meta Learning on Task Combinations
Robust Calibration with Multi-domain Temperature Scaling
Exploration via Planning for Information about the Optimal Trajectory
Mean Estimation in High-Dimensional Binary Markov Gaussian Mixture Models
BiT: Robustly Binarized Multi-distilled Transformer
PopArt: Efficient Sparse Regression and Experimental Design for Optimal Sparse Linear Bandits
On the Effect of Pre-training for Transformer in Different Modality on Offline Reinforcement Learning
On-Device Training Under 256KB Memory
Geo-SIC: Learning Deformable Geometric Shapes in Deep Image Classifiers
An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning
Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning
Finite-Time Analysis of Adaptive Temporal Difference Learning with Deep Neural Networks
Neural Surface Reconstruction of Dynamic Scenes with Monocular RGB-D Camera
Mutual Information Divergence: A Unified Metric for Multimodal Generative Models
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
GLIPv2: Unifying Localization and Vision-Language Understanding
A Unified Diversity Measure for Multiagent Reinforcement Learning
Plan To Predict: Learning an Uncertainty-Foreseeing Model For Model-Based Reinforcement Learning
Learning to Accelerate Partial Differential Equations via Latent Global Evolution
Active Learning for Multiple Target Models
Alignment-guided Temporal Attention for Video Action Recognition
Open-Ended Reinforcement Learning with Neural Reward Functions
On Margins and Generalisation for Voting Classifiers
Contrastive Neural Ratio Estimation
Mildly Conservative Q-Learning for Offline Reinforcement Learning
Self-Supervised Image Restoration with Blurry and Noisy Pairs
Recommender Forest for Efficient Retrieval
Retrieval-Augmented Diffusion Models
PatchComplete: Learning Multi-Resolution Patch Priors for 3D Shape Completion on Unseen Categories
Generalized Laplacian Eigenmaps
SAPA: Similarity-Aware Point Affiliation for Feature Upsampling
Expediting Large-Scale Vision Transformer for Dense Prediction without Fine-tuning
Random Sharpness-Aware Minimization
Generalized One-shot Domain Adaptation of Generative Adversarial Networks
SCONE: Surface Coverage Optimization in Unknown Environments by Volumetric Integration
A Quantitative Geometric Approach to Neural-Network Smoothness
Is this the Right Neighborhood? Accurate and Query Efficient Model Agnostic Explanations
Parametrically Retargetable Decision-Makers Tend To Seek Power
Learning Individualized Treatment Rules with Many Treatments: A Supervised Clustering Approach Using Adaptive Fusion
Differentially Private Model Compression
Is a Modular Architecture Enough?
Learning General World Models in a Handful of Reward-Free Deployments
Revisiting Heterophily For Graph Neural Networks
Recipe for a General, Powerful, Scalable Graph Transformer
CEIP: Combining Explicit and Implicit Priors for Reinforcement Learning with Demonstrations
GhostNetV2: Enhance Cheap Operation with Long-Range Attention
Elucidating the Design Space of Diffusion-Based Generative Models
Intermediate Prototype Mining Transformer for Few-Shot Semantic Segmentation
Robust Models are less Over-Confident
OST: Improving Generalization of DeepFake Detection via One-Shot Test-Time Training
KSD Aggregated Goodness-of-fit Test
Distributional Reward Estimation for Effective Multi-agent Deep Reinforcement Learning
A Near-Optimal Primal-Dual Method for Off-Policy Learning in CMDP
ZIN: When and How to Learn Invariance Without Environment Partition?
Enhance the Visual Representation via Discrete Adversarial Training
Frank-Wolfe-based Algorithms for Approximating Tyler's M-estimator
Spending Thinking Time Wisely: Accelerating MCTS with Virtual Expansions
Efficient and Effective Optimal Transport-Based Biclustering
SageMix: Saliency-Guided Mixup for Point Clouds
Heatmap Distribution Matching for Human Pose Estimation
Autoregressive Search Engines: Generating Substrings as Document Identifiers
Mirror Descent with Relative Smoothness in Measure Spaces, with application to Sinkhorn and EM
Deconfounded Representation Similarity for Comparison of Neural Networks
Rethinking Lipschitz Neural Networks and Certified Robustness: A Boolean Function Perspective
Fine-Grained Analysis of Stability and Generalization for Modern Meta Learning Algorithms
Gold-standard solutions to the Schrödinger equation using deep learning: How much physics do we need?
Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition
Out-of-Distribution Detection with An Adaptive Likelihood Ratio on Informative Hierarchical VAE
Relational Proxies: Emergent Relationships as Fine-Grained Discriminators
Sampling without Replacement Leads to Faster Rates in Finite-Sum Minimax Optimization
Unsupervised Cross-Task Generalization via Retrieval Augmentation
coVariance Neural Networks
On the inability of Gaussian process regression to optimally learn compositional functions
Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees
When to Update Your Model: Constrained Model-based Reinforcement Learning
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization
Constrained Langevin Algorithms with L-mixing External Random Variables
Practical Adversarial Multivalid Conformal Prediction
Biologically-Plausible Determinant Maximization Neural Networks for Blind Separation of Correlated Sources
Deep Generalized Schrödinger Bridge
Deep Generative Model for Periodic Graphs
Optimal Comparator Adaptive Online Learning with Switching Cost
Enhanced Bilevel Optimization via Bregman Distance
Learning State-Aware Visual Representations from Audible Interactions
Near-Optimal Multi-Agent Learning for Safe Coverage Control
Probabilistic Missing Value Imputation for Mixed Categorical and Ordered Data
Exploration via Elliptical Episodic Bonuses
GAUDI: A Neural Architect for Immersive 3D Scene Generation
Periodic Graph Transformers for Crystal Material Property Prediction
Parallel Tempering With a Variational Reference
On the consistent estimation of optimal Receiver Operating Characteristic (ROC) curve
NS3: Neuro-symbolic Semantic Code Search
A Deep Learning Dataloader with Shared Data Preparation
Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxies
Improving Variational Autoencoders with Density Gap-based Regularization
Fused Orthogonal Alternating Least Squares for Tensor Clustering
Representing Spatial Trajectories as Distributions
Model-Based Offline Reinforcement Learning with Pessimism-Modulated Dynamics Belief
CLEAR: Generative Counterfactual Explanations on Graphs
Wasserstein $K$-means for clustering probability distributions
Biologically-plausible backpropagation through arbitrary timespans via local neuromodulators
Cost-efficient Gaussian tensor network embeddings for tensor-structured inputs
Hub-Pathway: Transfer Learning from A Hub of Pre-trained Models
Green Hierarchical Vision Transformer for Masked Image Modeling
Beyond the Best: Distribution Functional Estimation in Infinite-Armed Bandits
An Investigation into Whitening Loss for Self-supervised Learning
Fixed-Distance Hamiltonian Monte Carlo
SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning
Category-Level 6D Object Pose Estimation in the Wild: A Semi-Supervised Learning Approach and A New Dataset
Deep Attentive Belief Propagation: Integrating Reasoning and Learning for Solving Constraint Optimization Problems
Amortized Mixing Coupling Processes for Clustering
HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions
Weakly supervised causal representation learning
Less-forgetting Multi-lingual Fine-tuning
Online Convex Optimization with Hard Constraints: Towards the Best of Two Worlds and Beyond
Rethinking Variational Inference for Probabilistic Programs with Stochastic Support
Retrieve, Reason, and Refine: Generating Accurate and Faithful Patient Instructions
Cross-modal Learning for Image-Guided Point Cloud Shape Completion
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels
FNeVR: Neural Volume Rendering for Face Animation
Bessel Equivariant Networks for Inversion of Transmission Effects in Multi-Mode Optical Fibres
Bidirectional Learning for Offline Infinite-width Model-based Optimization
TREC: Transient Redundancy Elimination-based Convolution
DivBO: Diversity-aware CASH for Ensemble Learning
Forecasting Human Trajectory from Scene History
Wasserstein Logistic Regression with Mixed Features
Contextual Bandits with Knapsacks for a Conversion Model
Diagnosing failures of fairness transfer across distribution shift in real-world medical settings
Adaptation Accelerating Sampling-based Bayesian Inference in Attractor Neural Networks
ELASTIC: Numerical Reasoning with Adaptive Symbolic Compiler
Oscillatory Tracking of Continuous Attractor Neural Networks Account for Phase Precession and Procession of Hippocampal Place Cells
UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANs
Make Sharpness-Aware Minimization Stronger: A Sparsified Perturbation Approach
Contrastive Learning as Goal-Conditioned Reinforcement Learning
Learning Viewpoint-Agnostic Visual Representations by Recovering Tokens in 3D Space
When are Local Queries Useful for Robust Learning?
Shield Decentralization for Safe Multi-Agent Reinforcement Learning
Extracting computational mechanisms from neural data using low-rank RNNs
Data Distributional Properties Drive Emergent In-Context Learning in Transformers
A Quadrature Rule combining Control Variates and Adaptive Importance Sampling
Dynamic Fair Division with Partial Information
Improved Imaging by Invex Regularizers with Global Optima Guarantees
Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients
Change-point Detection for Sparse and Dense Functional Data in General Dimensions
Sample-Efficient Learning of Correlated Equilibria in Extensive-Form Games
Local Spatiotemporal Representation Learning for Longitudinally-consistent Neuroimage Analysis
Parameter tuning and model selection in Optimal Transport with semi-dual Brenier formulation
Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systems
Generating multivariate time series with COmmon Source CoordInated GAN (COSCI-GAN)
Unsupervised Multi-View Object Segmentation Using Radiance Field Propagation
Online Deep Equilibrium Learning for Regularization by Denoising
When does return-conditioned supervised learning work for offline reinforcement learning?
Inductive Logical Query Answering in Knowledge Graphs
The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning
Biological Learning of Irreducible Representations of Commuting Transformations
The price of ignorance: how much does it cost to forget noise structure in low-rank matrix estimation?
MCVD - Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation
Bayesian Clustering of Neural Spiking Activity Using a Mixture of Dynamic Poisson Factor Analyzers
Non-convex online learning via algorithmic equivalence
Decomposing NeRF for Editing via Feature Field Distillation
Approximate Value Equivalence
Neur2SP: Neural Two-Stage Stochastic Programming
Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models
SparCL: Sparse Continual Learning on the Edge
Visual Prompting via Image Inpainting
Test-Time Training with Masked Autoencoders
Visual Clues: Bridging Vision and Language Foundations for Image Paragraph Captioning
Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization
BILCO: An Efficient Algorithm for Joint Alignment of Time Series
Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks
Falsification before Extrapolation in Causal Effect Estimation
LION: Latent Point Diffusion Models for 3D Shape Generation
FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction
Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions
Sharpness-Aware Training for Free
CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image Synthesis
3DILG: Irregular Latent Grids for 3D Generative Modeling
Translation-equivariant Representation in Recurrent Networks with a Continuous Manifold of Attractors
Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition
Towards Learning Universal Hyperparameter Optimizers with Transformers
OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression
ComGAN: Unsupervised Disentanglement and Segmentation via Image Composition
Non-Linear Coordination Graphs
Towards Hard-pose Virtual Try-on via 3D-aware Global Correspondence Learning
Fast Distance Oracles for Any Symmetric Norm
Low-rank Optimal Transport: Approximation, Statistics and Debiasing
Iterative Scene Graph Generation
Eliciting Thinking Hierarchy without a Prior
Learning Robust Rule Representations for Abstract Reasoning via Internal Inferences
Multi-layer State Evolution Under Random Convolutional Design
Latency-aware Spatial-wise Dynamic Networks
Margin-Based Few-Shot Class-Incremental Learning with Class-Level Overfitting Mitigation
Relation-Constrained Decoding for Text Generation
Searching for Better Spatio-temporal Alignment in Few-Shot Action Recognition
Could Giant Pre-trained Image Models Extract Universal Representations?
IM-Loss: Information Maximization Loss for Spiking Neural Networks
TokenMixup: Efficient Attention-guided Token-level Data Augmentation for Transformers
Hyperbolic Feature Augmentation via Distribution Estimation and Infinite Sampling on Manifolds
Verification and search algorithms for causal DAGs
AD-DROP: Attribution-Driven Dropout for Robust Language Model Fine-Tuning
Where to Pay Attention in Sparse Training for Feature Selection?
TA-MoE: Topology-Aware Large Scale Mixture-of-Expert Training
Understanding the Failure of Batch Normalization for Transformers in NLP
Transformers meet Stochastic Block Models: Attention with Data-Adaptive Sparsity and Cost
Theoretically Provable Spiking Neural Networks
Deep Combinatorial Aggregation
Transcormer: Transformer for Sentence Scoring with Sliding Language Modeling
Self-Supervised Learning with an Information Maximization Criterion
Improved Utility Analysis of Private CountSketch
A Classification of $G$-invariant Shallow Neural Networks
Module-Aware Optimization for Auxiliary Learning
Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering
Riemannian Score-Based Generative Modelling
Out-of-Distribution Detection via Conditional Kernel Independence Model
Towards Effective Multi-Modal Interchanges in Zero-Resource Sounding Object Localization
Policy Gradient With Serial Markov Chain Reasoning
Estimating graphical models for count data with applications to single-cell gene network
Improving 3D-aware Image Synthesis with A Geometry-aware Discriminator
Egocentric Video-Language Pretraining
Efficient Submodular Optimization under Noise: Local Search is Robust
Conservative Dual Policy Optimization for Efficient Model-Based Reinforcement Learning
DualCoOp: Fast Adaptation to Multi-Label Recognition with Limited Annotations
Does Momentum Change the Implicit Regularization on Separable Data?
VRL3: A Data-Driven Framework for Visual Deep Reinforcement Learning
Exact Solutions of a Deep Linear Network
ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning
Masked Prediction: A Parameter Identifiability View
Direct Advantage Estimation
Depth is More Powerful than Width with Prediction Concatenation in Deep Forest
AniFaceGAN: Animatable 3D-Aware Face Image Generation for Video Avatars
Instance-based Learning for Knowledge Base Completion
Efficient and Effective Augmentation Strategy for Adversarial Training
u-HuBERT: Unified Mixed-Modal Speech Pretraining And Zero-Shot Transfer to Unlabeled Modality
First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces
GENIE: Higher-Order Denoising Diffusion Solvers
Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy
Structured Recognition for Generative Models with Explaining Away
UniCLIP: Unified Framework for Contrastive Language-Image Pre-training
InsNet: An Efficient, Flexible, and Performant Insertion-based Text Generation Model
Local-Global MCMC kernels: the best of both worlds
Manifold Interpolating Optimal-Transport Flows for Trajectory Inference
Doubly Robust Counterfactual Classification
Uncertainty-Aware Reinforcement Learning for Risk-Sensitive Player Evaluation in Sports Game
Smooth Fictitious Play in Stochastic Games with Perturbed Payoffs and Unknown Transitions
SKFlow: Learning Optical Flow with Super Kernels
Non-stationary Bandits with Knapsacks
Weighted Mutual Learning with Diversity-Driven Model Compression
Learning to Attack Federated Learning: A Model-based Reinforcement Learning Attack Framework
Improving Zero-Shot Generalization in Offline Reinforcement Learning using Generalized Similarity Functions
Procedural Image Programs for Representation Learning
Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutation
High-Order Pooling for Graph Neural Networks with Tensor Decomposition
TTOpt: A Maximum Volume Quantized Tensor Train-based Optimization and its Application to Reinforcement Learning
SALSA: Attacking Lattice Cryptography with Transformers
Class-Aware Adversarial Transformers for Medical Image Segmentation
A Single-timescale Analysis for Stochastic Approximation with Multiple Coupled Sequences
You Only Live Once: Single-Life Reinforcement Learning
Semi-Supervised Learning with Decision Trees: Graph Laplacian Tree Alternating Optimization
When does dough become a bagel? Analyzing the remaining mistakes on ImageNet
Learning from Stochastically Revealed Preference
A Best-of-Both-Worlds Algorithm for Bandits with Delayed Feedback
Online Minimax Multiobjective Optimization: Multicalibeating and Other Applications
Algorithms that Approximate Data Removal: New Results and Limitations
Annihilation of Spurious Minima in Two-Layer ReLU Networks
Unsupervised Image-to-Image Translation with Density Changing Regularization
Reproducibility in Optimization: Theoretical Framework and Limits
Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering
Systematic improvement of neural network quantum states using Lanczos
Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss
Diagonal State Spaces are as Effective as Structured State Spaces
Why neural networks find simple solutions: The many regularizers of geometric complexity
Zero-Shot 3D Drug Design by Sketching and Generating
Adaptive Oracle-Efficient Online Learning
Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints
Efficient Active Learning with Abstention
Unsupervised Learning of Shape Programs with Repeatable Implicit Parts
Moderate-fitting as a Natural Backdoor Defender for Pre-trained Language Models
Controllable Text Generation with Neurally-Decomposed Oracle
A Fast Post-Training Pruning Framework for Transformers
ConfounderGAN: Protecting Image Data Privacy with Causal Confounder
Improved Feature Distillation via Projector Ensemble
Neuron with Steady Response Leads to Better Generalization
Mirror Descent Maximizes Generalized Margin and Can Be Implemented Efficiently
Self-Organized Group for Cooperative Multi-agent Reinforcement Learning
APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction
Learning Manifold Dimensions with Conditional Variational Autoencoders
Discovering Design Concepts for CAD Sketches
Reconstruction on Trees and Low-Degree Polynomials
Test Time Adaptation via Conjugate Pseudo-labels
Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning
GenerSpeech: Towards Style Transfer for Generalizable Out-Of-Domain Text-to-Speech
Momentum Adversarial Distillation: Handling Large Distribution Shifts in Data-Free Knowledge Distillation
FreGAN: Exploiting Frequency Components for Training GANs under Limited Data
FasterRisk: Fast and Accurate Interpretable Risk Scores
When to Trust Your Simulator: Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning
Generalization Bounds for Stochastic Gradient Descent via Localized $\varepsilon$-Covers
Symbolic Distillation for Learned TCP Congestion Control
Proximal Learning With Opponent-Learning Awareness
Accelerated Linearized Laplace Approximation for Bayesian Deep Learning
GAGA: Deciphering Age-path of Generalized Self-paced Regularizer
Provable Benefit of Multitask Representation Learning in Reinforcement Learning
Follow-the-Perturbed-Leader for Adversarial Markov Decision Processes with Bandit Feedback
Why do We Need Large Batchsizes in Contrastive Learning? A Gradient-Bias Perspective
Globally Convergent Policy Search for Output Estimation
To update or not to update? Neurons at equilibrium in deep models
Grow and Merge: A Unified Framework for Continuous Categories Discovery
OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Clouds
Learning a Condensed Frame for Memory-Efficient Video Class-Incremental Learning
Factorized-FL: Personalized Federated Learning with Parameter Factorization & Similarity Matching
Autoinverse: Uncertainty Aware Inversion of Neural Networks
Bootstrapped Transformer for Offline Reinforcement Learning
Double Check Your State Before Trusting It: Confidence-Aware Bidirectional Offline Model-Based Imagination
Fair Wrapping for Black-box Predictions
GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networks
Generic bounds on the approximation error for physics-informed (and) operator learning
Debiased, Longitudinal and Coordinated Drug Recommendation through Multi-Visit Clinic Records
Most Activation Functions Can Win the Lottery Without Excessive Depth
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
Truncated Matrix Power Iteration for Differentiable DAG Learning
Robust Rent Division
Temporally Disentangled Representation Learning
Improving Transformer with an Admixture of Attention Heads
Para-CFlows: $C^k$-universal diffeomorphism approximators as superior neural surrogates
TA-GATES: An Encoding Scheme for Neural Network Architectures
Gradient Methods Provably Converge to Non-Robust Networks
Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited
Mask-based Latent Reconstruction for Reinforcement Learning
MaskPlace: Fast Chip Placement via Reinforced Visual Representation Learning
SwinTrack: A Simple and Strong Baseline for Transformer Tracking
Self-supervised Amodal Video Object Segmentation
Improving Generative Adversarial Networks via Adversarial Learning in Latent Space
EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization
First is Better Than Last for Language Data Influence
Molecule Generation by Principal Subgraph Mining and Assembling
Conditional Independence Testing with Heteroskedastic Data and Applications to Causal Discovery
Equivariant Graph Hierarchy-Based Neural Networks
Semi-infinitely Constrained Markov Decision Processes
One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement
Bridge the Gap Between Architecture Spaces via A Cross-Domain Predictor
Mingling Foresight with Imagination: Model-Based Cooperative Multi-Agent Reinforcement Learning
Top Two Algorithms Revisited
Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum
A Probabilistic Graph Coupling View of Dimension Reduction
Knowledge Distillation Improves Graph Structure Augmentation for Graph Neural Networks
LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent Reinforcement Learning
Stimulative Training of Residual Networks: A Social Psychology Perspective of Loafing
MExMI: Pool-based Active Model Extraction Crossover Membership Inference
S3GC: Scalable Self-Supervised Graph Clustering
Parameter-free Dynamic Graph Embedding for Link Prediction
Federated Submodel Optimization for Hot and Cold Data Features
Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization?
Causality Preserving Chaotic Transformation and Classification using Neurochaos Learning
On Margin Maximization in Linear and ReLU Networks
Optimal Binary Classification Beyond Accuracy
Active Learning of Classifiers with Label and Seed Queries
AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition
FedPop: A Bayesian Approach for Personalised Federated Learning
Learning to Drop Out: An Adversarial Approach to Training Sequence VAEs
Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning
Effective Adaptation in Multi-Task Co-Training for Unified Autonomous Driving
Large Language Models are Zero-Shot Reasoners
Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks
Amplifying Membership Exposure via Data Poisoning
Robust Graph Structure Learning via Multiple Statistical Tests
Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks
Learning to Constrain Policy Optimization with Virtual Trust Region
Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation
NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification
Efficient Architecture Search for Diverse Tasks
GRASP: Navigating Retrosynthetic Planning with Goal-driven Policy
Multi-Agent Reinforcement Learning is a Sequence Modeling Problem
Distributed Learning of Conditional Quantiles in the Reproducing Kernel Hilbert Space
The Policy-gradient Placement and Generative Routing Neural Networks for Chip Design
Pruning Neural Networks via Coresets and Convex Geometry: Towards No Assumptions
Revisiting Injective Attacks on Recommender Systems
On the Convergence of Stochastic Multi-Objective Gradient Manipulation and Beyond
Learning to Generate Inversion-Resistant Model Explanations
Semi-Supervised Generative Models for Multiagent Trajectories
Unknown-Aware Domain Adversarial Learning for Open-Set Domain Adaptation
Distributionally Robust Optimization via Ball Oracle Acceleration
DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep Learning
Domain Adaptation under Open Set Label Shift
Generalization Bounds with Minimal Dependency on Hypothesis Class via Distributionally Robust Optimization
NeMF: Neural Motion Fields for Kinematic Animation
On Robust Multiclass Learnability
Moment Distributionally Robust Tree Structured Prediction
Alleviating "Posterior Collapse'' in Deep Topic Models via Policy Gradient
Grounding Aleatoric Uncertainty for Unsupervised Environment Design
Conditional Meta-Learning of Linear Representations
AZ-whiteness test: a test for signal uncorrelation on spatio-temporal graphs
Sample-Then-Optimize Batch Neural Thompson Sampling
Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learning
Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination
EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equations
Blessing of Depth in Linear Regression: Deeper Models Have Flatter Landscape Around the True Solution
PKD: General Distillation Framework for Object Detectors via Pearson Correlation Coefficient
Star Temporal Classification: Sequence Modeling with Partially Labeled Data
Neural Stochastic Control
Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor
Adaptive Sampling for Discovery
Diverse Weight Averaging for Out-of-Distribution Generalization
Counterfactual Temporal Point Processes
Sparse Winning Tickets are Data-Efficient Image Recognizers
Improved Regret Analysis for Variance-Adaptive Linear Bandits and Horizon-Free Linear Mixture MDPs
Reduction Algorithms for Persistence Diagrams of Networks: CoralTDA and PrunIT
Approximation with CNNs in Sobolev Space: with Applications to Classification
Tracking Functional Changes in Nonstationary Signals with Evolutionary Ensemble Bayesian Model for Robust Neural Decoding
A Unified Convergence Theorem for Stochastic Optimization Methods
On Embeddings for Numerical Features in Tabular Deep Learning
Near-Optimal Collaborative Learning in Bandits
Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces
Iron: Private Inference on Transformers
Towards Disentangling Information Paths with Coded ResNeXt
Adaptive Multi-stage Density Ratio Estimation for Learning Latent Space Energy-based Model
Flamingo: a Visual Language Model for Few-Shot Learning
Learning to Re-weight Examples with Optimal Transport for Imbalanced Classification
ZooD: Exploiting Model Zoo for Out-of-Distribution Generalization
Torsional Diffusion for Molecular Conformer Generation
Beyond Time-Average Convergence: Near-Optimal Uncoupled Online Learning via Clairvoyant Multiplicative Weights Update
Approximate Euclidean lengths and distances beyond Johnson-Lindenstrauss
A consistently adaptive trust-region method
Order-Invariant Cardinality Estimators Are Differentially Private
Spectral Bias in Practice: The Role of Function Frequency in Generalization
Task-level Differentially Private Meta Learning
Distributed Inverse Constrained Reinforcement Learning for Multi-agent Systems
WaveBound: Dynamic Error Bounds for Stable Time Series Forecasting
Self-Supervised Learning of Brain Dynamics from Broad Neuroimaging Data
Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs
Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization
Log-Polar Space Convolution Layers
Efficient Training of Low-Curvature Neural Networks
Self-Supervised Fair Representation Learning without Demographics
Nonlinear MCMC for Bayesian Machine Learning
Scale-invariant Learning by Physics Inversion
On Non-Linear operators for Geometric Deep Learning
A Geometric Perspective on Variational Autoencoders
Contrastive Graph Structure Learning via Information Bottleneck for Recommendation
Iterative Structural Inference of Directed Graphs
PDSketch: Integrated Domain Programming, Learning, and Planning
Off-Policy Evaluation with Policy-Dependent Optimization Response
Interpolation and Regularization for Causal Learning
Confidence-based Reliable Learning under Dual Noises
Dynamic Inverse Reinforcement Learning for Characterizing Animal Behavior
DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing
Black-Box Generalization: Stability of Zeroth-Order Learning
Label-Aware Global Consistency for Multi-Label Learning with Single Positive Labels
Emergent Communication: Generalization and Overfitting in Lewis Games
Latent Planning via Expansive Tree Search
Near-Optimal Regret for Adversarial MDP with Delayed Bandit Feedback
RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer
The Phenomenon of Policy Churn
Optimal-er Auctions through Attention
Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space
Defending Against Adversarial Attacks via Neural Dynamic System
Association Graph Learning for Multi-Task Classification with Category Shifts
Weakly Supervised Representation Learning with Sparse Perturbations
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Learning Superpoint Graph Cut for 3D Instance Segmentation
First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization
CyCLIP: Cyclic Contrastive Language-Image Pretraining
Amortized Inference for Heterogeneous Reconstruction in Cryo-EM
Neural Stochastic PDEs: Resolution-Invariant Learning of Continuous Spatiotemporal Dynamics
Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent
Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF
An $\alpha$-No-Regret Algorithm For Graphical Bilinear Bandits
Perfect Sampling from Pairwise Comparisons
Value Function Decomposition for Iterative Design of Reinforcement Learning Agents
Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations
VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programming
Conformal Off-Policy Prediction in Contextual Bandits
Constrained Update Projection Approach to Safe Policy Optimization
Global Linear and Local Superlinear Convergence of IRLS for Non-Smooth Robust Regression
A Fourier Approach to Mixture Learning
LECO: Learnable Episodic Count for Task-Specific Intrinsic Reward
Domain Generalization without Excess Empirical Risk
Navigating Memory Construction by Global Pseudo-Task Simulation for Continual Learning
Optimal Transport of Classifiers to Fairness
FedSR: A Simple and Effective Domain Generalization Method for Federated Learning
Using Partial Monotonicity in Submodular Maximization
When Do Flat Minima Optimizers Work?
Revisiting Non-Parametric Matching Cost Volumes for Robust and Generalizable Stereo Matching
Large-scale Optimization of Partial AUC in a Range of False Positive Rates
Learning in Congestion Games with Bandit Feedback
TreeMoCo: Contrastive Neuron Morphology Representation Learning
Near-Optimal Sample Complexity Bounds for Constrained MDPs
Fairness Transferability Subject to Bounded Distribution Shift
The Burer-Monteiro SDP method can fail even above the Barvinok-Pataki bound
WeightedSHAP: analyzing and improving Shapley based feature attributions
How to talk so AI will learn: Instructions, descriptions, and autonomy
Improved Algorithms for Neural Active Learning
Global Convergence of Direct Policy Search for State-Feedback $\mathcal{H}_\infty$ Robust Control: A Revisit of Nonsmooth Synthesis with Goldstein Subdifferential
Nonlinear Sufficient Dimension Reduction with a Stochastic Neural Network
Bayesian inference via sparse Hamiltonian flows
On Batch Teaching with Sample Complexity Bounded by VCD
AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments
Model-based Lifelong Reinforcement Learning with Bayesian Exploration
projUNN: efficient method for training deep networks with unitary matrices
Staggered Rollout Designs Enable Causal Inference Under Interference Without Network Knowledge
KERPLE: Kernelized Relative Positional Embedding for Length Extrapolation
An Information-Theoretic Framework for Deep Learning
ORIENT: Submodular Mutual Information Measures for Data Subset Selection under Distribution Shift
Insights into Pre-training via Simpler Synthetic Tasks
Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions
Density-driven Regularization for Out-of-distribution Detection
Rapid Model Architecture Adaption for Meta-Learning
Finding Correlated Equilibrium of Constrained Markov Game: A Primal-Dual Approach
Hyperbolic Embedding Inference for Structured Multi-Label Prediction
AutoMTL: A Programming Framework for Automating Efficient Multi-Task Learning
Wavelet Feature Maps Compression for Image-to-Image CNNs
CoPur: Certifiably Robust Collaborative Inference via Feature Purification
Interventions, Where and How? Experimental Design for Causal Models at Scale
Efficient Non-Parametric Optimizer Search for Diverse Tasks
Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers
Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis
Scaling Multimodal Pre-Training via Cross-Modality Gradient Harmonization
A Character-Level Length-Control Algorithm for Non-Autoregressive Sentence Summarization
The Privacy Onion Effect: Memorization is Relative
Recursive Reasoning in Minimax Games: A Level $k$ Gradient Play Method
Deep Ensembles Work, But Are They Necessary?
Tight Lower Bounds on Worst-Case Guarantees for Zero-Shot Learning with Attributes
Variational Model Perturbation for Source-Free Domain Adaptation
Generative multitask learning mitigates target-causing confounding
Draft-and-Revise: Effective Image Generation with Contextual RQ-Transformer
Acceleration in Distributed Sparse Regression
Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium
Repairing Neural Networks by Leaving the Right Past Behind
[Re] Privacy-preserving collaborative learning with automatic transformation search
Sequence Model Imitation Learning with Unobserved Contexts
GULP: a prediction-based metric between representations
Efficient Frameworks for Generalized Low-Rank Matrix Bandit Problems
Composition Theorems for Interactive Differential Privacy
On the Global Convergence Rates of Decentralized Softmax Gradient Play in Markov Potential Games
Robust Generalized Method of Moments: A Finite Sample Viewpoint
Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness
Regret Bounds for Risk-Sensitive Reinforcement Learning
Semi-supervised Active Linear Regression
Near-Isometric Properties of Kronecker-Structured Random Tensor Embeddings
Riemannian Diffusion Models
Towards Safe Reinforcement Learning with a Safety Editor Policy
On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach
The Implicit Delta Method
Bellman Residual Orthogonalization for Offline Reinforcement Learning
Meta-Learning Dynamics Forecasting Using Task Inference
On Scrambling Phenomena for Randomly Initialized Recurrent Networks
Data-Efficient Augmentation for Training Neural Networks
Beyond black box densities: Parameter learning for the deviated components
Robust Learning against Relational Adversaries
Policy Optimization for Markov Games: Unified Framework and Faster Convergence
Continuously Tempered PDMP samplers
Uncalibrated Models Can Improve Human-AI Collaboration
Few-Shot Non-Parametric Learning with Deep Latent Variable Model
Emergent Graphical Conventions in a Visual Communication Game
Chain of Thought Imitation with Procedure Cloning
Conformalized Fairness via Quantile Regression
Improving Self-Supervised Learning by Characterizing Idealized Representations
Learning Options via Compression
Rapidly Mixing Multiple-try Metropolis Algorithms for Model Selection Problems
Understanding Hyperdimensional Computing for Parallel Single-Pass Learning
Functional Indirection Neural Estimator for Better Out-of-distribution Generalization
Few-shot Learning for Feature Selection with Hilbert-Schmidt Independence Criterion
Bayesian Spline Learning for Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty
Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity
Active Learning Polynomial Threshold Functions
Efficient and Near-Optimal Smoothed Online Learning for Generalized Linear Functions
Signal Propagation in Transformers: Theoretical Perspectives and the Role of Rank Collapse
An Analysis of Ensemble Sampling
Towards Understanding the Condensation of Neural Networks at Initial Training
Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting
Optimal Scaling for Locally Balanced Proposals in Discrete Spaces
End-to-end Algorithm Synthesis with Recurrent Networks: Extrapolation without Overthinking
Domain Adaptation meets Individual Fairness. And they get along.
Free Probability for predicting the performance of feed-forward fully connected neural networks
Conformal Prediction with Temporal Quantile Adjustments
Using natural language and program abstractions to instill human inductive biases in machines
Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning
Neurosymbolic Deep Generative Models for Sequence Data with Relational Constraints
Polynomial time guarantees for the Burer-Monteiro method
Scalable design of Error-Correcting Output Codes using Discrete Optimization with Graph Coloring
On Deep Generative Models for Approximation and Estimation of Distributions on Manifolds
Nest Your Adaptive Algorithm for Parameter-Agnostic Nonconvex Minimax Optimization
Simple Unsupervised Object-Centric Learning for Complex and Naturalistic Videos
Revisiting Optimal Convergence Rate for Smooth and Non-convex Stochastic Decentralized Optimization
Physics-Informed Implicit Representations of Equilibrium Network Flows
Learning Generalized Policy Automata for Relational Stochastic Shortest Path Problems
Simplified Graph Convolution with Heterophily
DMAP: a Distributed Morphological Attention Policy for learning to locomote with a changing body
Byzantine-tolerant federated Gaussian process regression for streaming data
Distributionally Adaptive Meta Reinforcement Learning
Submodular Maximization in Clean Linear Time
Amortized Proximal Optimization
On Learning Fairness and Accuracy on Multiple Subgroups
HUMUS-Net: Hybrid Unrolled Multi-scale Network Architecture for Accelerated MRI Reconstruction
On the Symmetries of Deep Learning Models and their Internal Representations
Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees
Second Thoughts are Best: Learning to Re-Align With Human Values from Text Edits
Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial Removal
FourierFormer: Transformer Meets Generalized Fourier Integral Theorem
In What Ways Are Deep Neural Networks Invariant and How Should We Measure This?
Faster and Scalable Algorithms for Densest Subgraph and Decomposition
Co-Modality Graph Contrastive Learning for Imbalanced Node Classification
ACIL: Analytic Class-Incremental Learning with Absolute Memorization and Privacy Protection
Zeroth-Order Hard-Thresholding: Gradient Error vs. Expansivity
Non-Linguistic Supervision for Contrastive Learning of Sentence Embeddings
A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models
A General Framework for Auditing Differentially Private Machine Learning
Minimax Optimal Algorithms for Fixed-Budget Best Arm Identification
Pruning’s Effect on Generalization Through the Lens of Training and Regularization
Kernel similarity matching with Hebbian networks
QC-StyleGAN - Quality Controllable Image Generation and Manipulation
Human-AI Shared Control via Policy Dissection
Label-invariant Augmentation for Semi-Supervised Graph Classification
Preservation of the Global Knowledge by Not-True Distillation in Federated Learning
Using Embeddings for Causal Estimation of Peer Influence in Social Networks
A Unifying Framework of Off-Policy General Value Function Evaluation
TaSIL: Taylor Series Imitation Learning
Asymptotic Behaviors of Projected Stochastic Approximation: A Jump Diffusion Perspective
VF-PS: How to Select Important Participants in Vertical Federated Learning, Efficiently and Securely?
LISA: Learning Interpretable Skill Abstractions from Language
NSNet: A General Neural Probabilistic Framework for Satisfiability Problems
Model Preserving Compression for Neural Networks
Effects of Data Geometry in Early Deep Learning
Minimax-Optimal Multi-Agent RL in Markov Games With a Generative Model
Tight Mutual Information Estimation With Contrastive Fenchel-Legendre Optimization
Pre-Trained Model Reusability Evaluation for Small-Data Transfer Learning
Graph Few-shot Learning with Task-specific Structures
Offline Goal-Conditioned Reinforcement Learning via $f$-Advantage Regression
Old can be Gold: Better Gradient Flow can Make Vanilla-GCNs Great Again
Faster Deep Reinforcement Learning with Slower Online Network
An Asymptotically Optimal Batched Algorithm for the Dueling Bandit Problem
Beyond Not-Forgetting: Continual Learning with Backward Knowledge Transfer
Distributed Distributionally Robust Optimization with Non-Convex Objectives
Continuous Deep Q-Learning in Optimal Control Problems: Normalized Advantage Functions Analysis
Merging Models with Fisher-Weighted Averaging
Path Independent Equilibrium Models Can Better Exploit Test-Time Computation
Private Graph All-Pairwise-Shortest-Path Distance Release with Improved Error Rate
A Theory of PAC Learnability under Transformation Invariances
Global Convergence of Federated Learning for Mixed Regression
Segmenting Moving Objects via an Object-Centric Layered Representation
Invariance Learning based on Label Hierarchy
Online Algorithms for the Santa Claus Problem
Federated Learning from Pre-Trained Models: A Contrastive Learning Approach
When are Offline Two-Player Zero-Sum Markov Games Solvable?
Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks
Provably Efficient Offline Multi-agent Reinforcement Learning via Strategy-wise Bonus
Implicit Bias of Gradient Descent on Reparametrized Models: On Equivalence to Mirror Descent
Few-Shot Audio-Visual Learning of Environment Acoustics
Redundancy-Free Message Passing for Graph Neural Networks
SemMAE: Semantic-Guided Masking for Learning Masked Autoencoders
Weighted Distillation with Unlabeled Examples
Mixture-of-Experts with Expert Choice Routing
The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models
Diffusion-LM Improves Controllable Text Generation
Self-Supervised Pretraining for Large-Scale Point Clouds
Invariant and Transportable Representations for Anti-Causal Domain Shifts
Sparsity in Continuous-Depth Neural Networks
A Variational Edge Partition Model for Supervised Graph Representation Learning
A Simple Approach to Automated Spectral Clustering
Point Transformer V2: Grouped Vector Attention and Partition-based Pooling
Accelerated Training of Physics-Informed Neural Networks (PINNs) using Meshless Discretizations
Fault-Aware Neural Code Rankers
PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization
A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal
Off-Policy Evaluation for Episodic Partially Observable Markov Decision Processes under Non-Parametric Models
Learning Symmetric Rules with SATNet
Consistent Interpolating Ensembles via the Manifold-Hilbert Kernel
Get More at Once: Alternating Sparse Training with Gradient Correction
Learning Fractional White Noises in Neural Stochastic Differential Equations
“Why Not Other Classes?”: Towards Class-Contrastive Back-Propagation Explanations
Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power
Training with More Confidence: Mitigating Injected and Natural Backdoors During Training
Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments
Batch Multi-Fidelity Active Learning with Budget Constraints
Between Stochastic and Adversarial Online Convex Optimization: Improved Regret Bounds via Smoothness
Time-Conditioned Dances with Simplicial Complexes: Zigzag Filtration Curve based Supra-Hodge Convolution Networks for Time-series Forecasting
Constrained Stochastic Nonconvex Optimization with State-dependent Markov Data
Integral Probability Metrics PAC-Bayes Bounds
Skills Regularized Task Decomposition for Multi-task Offline Reinforcement Learning
M2N: Mesh Movement Networks for PDE Solvers
Exploiting the Relationship Between Kendall's Rank Correlation and Cosine Similarity for Attribution Protection
Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation
Gaussian Copula Embeddings
Transferring Pre-trained Multimodal Representations with Cross-modal Similarity Matching
CoNSoLe: Convex Neural Symbolic Learning
Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees
Meta-Auto-Decoder for Solving Parametric Partial Differential Equations
Non-Stationary Bandits under Recharging Payoffs: Improved Planning with Sublinear Regret
Long-Form Video-Language Pre-Training with Multimodal Temporal Contrastive Learning
PlasticityNet: Learning to Simulate Metal, Sand, and Snow for Optimization Time Integration
[Re] Value Alignment Verification
Nearly-Tight Bounds for Testing Histogram Distributions
Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination
Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picture
Reinforcement Learning with Automated Auxiliary Loss Search
Tractable Function-Space Variational Inference in Bayesian Neural Networks
Are all Frames Equal? Active Sparse Labeling for Video Action Detection
Unsupervised Learning under Latent Label Shift
You Can’t Count on Luck: Why Decision Transformers and RvS Fail in Stochastic Environments
Provable Subspace Identification Under Post-Nonlinear Mixtures
Truly Deterministic Policy Optimization
Active Learning Helps Pretrained Models Learn the Intended Task
A Consolidated Cross-Validation Algorithm for Support Vector Machines via Data Reduction
Giving Feedback on Interactive Student Programs with Meta-Exploration
On Leave-One-Out Conditional Mutual Information For Generalization
High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation
Global Optimal K-Medoids Clustering of One Million Samples
A Scalable Deterministic Global Optimization Algorithm for Training Optimal Decision Tree
What Can Transformers Learn In-Context? A Case Study of Simple Function Classes
GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis
Provably Feedback-Efficient Reinforcement Learning via Active Reward Learning
Data-Driven Offline Decision-Making via Invariant Representation Learning
When Does Group Invariant Learning Survive Spurious Correlations?
On Elimination Strategies for Bandit Fixed-Confidence Identification
So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems
Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels
DHRL: A Graph-Based Approach for Long-Horizon and Sparse Hierarchical Reinforcement Learning
Improving Diffusion Models for Inverse Problems using Manifold Constraints
DARE: Disentanglement-Augmented Rationale Extraction
Symmetry-induced Disentanglement on Graphs
Learning in Observable POMDPs, without Computationally Intractable Oracles
When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning
Spherization Layer: Representation Using Only Angles
Grounded Reinforcement Learning: Learning to Win the Game under Human Commands
How Powerful are K-hop Message Passing Graph Neural Networks
MEMO: Test Time Robustness via Adaptation and Augmentation
Redundant representations help generalization in wide neural networks
Dynamic Learning in Large Matching Markets
Near-Optimal Goal-Oriented Reinforcement Learning in Non-Stationary Environments
Towards Understanding the Mixture-of-Experts Layer in Deep Learning
A time-resolved theory of information encoding in recurrent neural networks
Coresets for Relational Data and The Applications
Coresets for Wasserstein Distributionally Robust Optimization Problems
Lazy and Fast Greedy MAP Inference for Determinantal Point Process
FlowHMM: Flow-based continuous hidden Markov models
Max-Min Off-Policy Actor-Critic Method Focusing on Worst-Case Robustness to Model Misspecification
An Adaptive Deep RL Method for Non-Stationary Environments with Piecewise Stable Context
Experimental Design for Linear Functionals in Reproducing Kernel Hilbert Spaces
Imbalance Trouble: Revisiting Neural-Collapse Geometry
Injecting Domain Knowledge from Empirical Interatomic Potentials to Neural Networks for Predicting Material Properties
How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders
WT-MVSNet: Window-based Transformers for Multi-view Stereo
Models Out of Line: A Fourier Lens on Distribution Shift Robustness
SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction
Chromatic Correlation Clustering, Revisited
A Reduction to Binary Approach for Debiasing Multiclass Datasets
MetricFormer: A Unified Perspective of Correlation Exploring in Similarity Learning
Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays
Revisiting Neural Scaling Laws in Language and Vision
Towards Consistency in Adversarial Classification
Last-Iterate Convergence of Optimistic Gradient Method for Monotone Variational Inequalities
Graph Convolution Network based Recommender Systems: Learning Guarantee and Item Mixture Powered Strategy
Revisit last-iterate convergence of mSGD under milder requirement on step size
Non-Monotonic Latent Alignments for CTC-Based Non-Autoregressive Machine Translation
Joint Learning of 2D-3D Weakly Supervised Semantic Segmentation
Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies Reconstruction
Optimal Positive Generation via Latent Transformation for Contrastive Learning
Neural-Symbolic Entangled Framework for Complex Query Answering
Multiagent Q-learning with Sub-Team Coordination
Sound and Complete Verification of Polynomial Networks
Laplacian Autoencoders for Learning Stochastic Representations
Oracle Inequalities for Model Selection in Offline Reinforcement Learning
Revisiting Active Sets for Gaussian Process Decoders
Bounding and Approximating Intersectional Fairness through Marginal Fairness
MAtt: A Manifold Attention Network for EEG Decoding
BinauralGrad: A Two-Stage Conditional Diffusion Probabilistic Model for Binaural Audio Synthesis
Collaborative Decision Making Using Action Suggestions
Dynamics of SGD with Stochastic Polyak Stepsizes: Truly Adaptive Variants and Convergence to Exact Solution
A gradient estimator via L1-randomization for online zero-order optimization with two point feedback
Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization
Selective compression learning of latent representations for variable-rate image compression
Neural Network Architecture Beyond Width and Depth
On the relationship between variational inference and auto-associative memory
Sparse Probabilistic Circuits via Pruning and Growing
When to Intervene: Learning Optimal Intervention Policies for Critical Events
Smoothed Embeddings for Certified Few-Shot Learning
An Analytical Theory of Curriculum Learning in Teacher-Student Networks
Black-box coreset variational inference
Distilling Representations from GAN Generator via Squeeze and Span
Generalization Analysis of Message Passing Neural Networks on Large Random Graphs
Meta-Learning with Self-Improving Momentum Target
Reinforcement Learning in a Birth and Death Process: Breaking the Dependence on the State Space
Sequence-to-Set Generative Models
What Makes Graph Neural Networks Miscalibrated?
A Win-win Deal: Towards Sparse and Robust Pre-trained Language Models
Consistency of Constrained Spectral Clustering under Graph Induced Fair Planted Partitions
A Regret-Variance Trade-Off in Online Learning
Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning
Not too little, not too much: a theoretical analysis of graph (over)smoothing
On A Mallows-type Model For (Ranked) Choices
Inverse Design for Fluid-Structure Interactions using Graph Network Simulators
Towards a Standardised Performance Evaluation Protocol for Cooperative MARL
On the Learning Mechanisms in Physical Reasoning
Joint Entropy Search For Maximally-Informed Bayesian Optimization
Benign Overfitting in Two-layer Convolutional Neural Networks
Proximal Point Imitation Learning
On the Robustness of Graph Neural Diffusion to Topology Perturbations
Power and limitations of single-qubit native quantum neural networks
A Characterization of Semi-Supervised Adversarially Robust PAC Learnability
Accelerating SGD for Highly Ill-Conditioned Huge-Scale Online Matrix Completion
Accelerated Primal-Dual Gradient Method for Smooth and Convex-Concave Saddle-Point Problems with Bilinear Coupling
Distilled Gradient Aggregation: Purify Features for Input Attribution in the Deep Neural Network
Sequential Information Design: Learning to Persuade in the Dark
Optimal Weak to Strong Learning
Unsupervised Learning of Group Invariant and Equivariant Representations
On the Approximation of Cooperative Heterogeneous Multi-Agent Reinforcement Learning (MARL) using Mean Field Control (MFC)
Estimating the Arc Length of the Optimal ROC Curve and Lower Bounding the Maximal AUC
Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounders
A Reparametrization-Invariant Sharpness Measure Based on Information Geometry
Bayesian Active Learning with Fully Bayesian Gaussian Processes
Log-Concave and Multivariate Canonical Noise Distributions for Differential Privacy
On Measuring Excess Capacity in Neural Networks
General Cutting Planes for Bound-Propagation-Based Neural Network Verification
Unsupervised Adaptation from Repeated Traversals for Autonomous Driving
Fine-Grained Semantically Aligned Vision-Language Pre-Training
On Sample Optimality in Personalized Collaborative and Federated Learning
Using Mixup as a Regularizer Can Surprisingly Improve Accuracy & Out-of-Distribution Robustness
Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning
A Variant of Anderson Mixing with Minimal Memory Size
Augmented RBMLE-UCB Approach for Adaptive Control of Linear Quadratic Systems
Improved techniques for deterministic l2 robustness
Real-Valued Backpropagation is Unsuitable for Complex-Valued Neural Networks
Anonymized Histograms in Intermediate Privacy Models
Relaxing Equivariance Constraints with Non-stationary Continuous Filters
MCL-GAN: Generative Adversarial Networks with Multiple Specialized Discriminators
Sparse Gaussian Process Hyperparameters: Optimize or Integrate?
Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch
Learning to Branch with Tree MDPs
Fine-tuning Language Models over Slow Networks using Activation Quantization with Guarantees
Probing Classifiers are Unreliable for Concept Removal and Detection
Graph Learning Assisted Multi-Objective Integer Programming
Randomized Sketches for Clustering: Fast and Optimal Kernel $k$-Means
Escaping Saddle Points for Effective Generalization on Class-Imbalanced Data
Certifying Robust Graph Classification under Orthogonal Gromov-Wasserstein Threats
On the Representation Collapse of Sparse Mixture of Experts
Information bottleneck theory of high-dimensional regression: relevancy, efficiency and optimality
Statistically Meaningful Approximation: a Case Study on Approximating Turing Machines with Transformers
A Data-Augmentation Is Worth A Thousand Samples: Analytical Moments And Sampling-Free Training
Partial Identification of Treatment Effects with Implicit Generative Models
Learning Neural Acoustic Fields
Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning
Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions
A contrastive rule for meta-learning
Meta Reinforcement Learning with Finite Training Tasks - a Density Estimation Approach
A gradient sampling method with complexity guarantees for Lipschitz functions in high and low dimensions
Regularized Molecular Conformation Fields
You Never Stop Dancing: Non-freezing Dance Generation via Bank-constrained Manifold Projection
Risk-Driven Design of Perception Systems
Langevin Autoencoders for Learning Deep Latent Variable Models
Neural Estimation of Submodular Functions with Applications to Differentiable Subset Selection
Learning on Arbitrary Graph Topologies via Predictive Coding
Multi-Lingual Acquisition on Multimodal Pre-training for Cross-modal Retrieval
Semantic Exploration from Language Abstractions and Pretrained Representations
A Unified Sequence Interface for Vision Tasks
Is Integer Arithmetic Enough for Deep Learning Training?
Confident Adaptive Language Modeling
Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces
3D Concept Grounding on Neural Fields
A Solver-free Framework for Scalable Learning in Neural ILP Architectures
Unsupervised Object Representation Learning using Translation and Rotation Group Equivariant VAE
Beyond Rewards: a Hierarchical Perspective on Offline Multiagent Behavioral Analysis
Luckiness in Multiscale Online Learning
Effective Dimension in Bandit Problems under Censorship
In Defense of the Unitary Scalarization for Deep Multi-Task Learning
Beyond IID: data-driven decision-making in heterogeneous environments
Scalable Multi-agent Covering Option Discovery based on Kronecker Graphs
Private Multiparty Perception for Navigation
Group Meritocratic Fairness in Linear Contextual Bandits
Deep Equilibrium Approaches to Diffusion Models
Addressing Leakage in Concept Bottleneck Models
Evolution of Neural Tangent Kernels under Benign and Adversarial Training
The least-control principle for local learning at equilibrium
Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps
Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers
PhysGNN: A Physics--Driven Graph Neural Network Based Model for Predicting Soft Tissue Deformation in Image--Guided Neurosurgery
Archimedes Meets Privacy: On Privately Estimating Quantiles in High Dimensions Under Minimal Assumptions
Better SGD using Second-order Momentum
Learning from Few Samples: Transformation-Invariant SVMs with Composition and Locality at Multiple Scales
DevFly: Bio-Inspired Development of Binary Connections for Locality Preserving Sparse Codes
Multi-agent Dynamic Algorithm Configuration
Predictive Coding beyond Gaussian Distributions
Jump Self-attention: Capturing High-order Statistics in Transformers
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations
RISE: Robust Individualized Decision Learning with Sensitive Variables
Efficient and Stable Fully Dynamic Facility Location
Envy-free Policy Teaching to Multiple Agents
VaiPhy: a Variational Inference Based Algorithm for Phylogeny
Active Learning with Safety Constraints
Trustworthy Monte Carlo
Learning-Augmented Algorithms for Online Linear and Semidefinite Programming
Near-Optimal Correlation Clustering with Privacy
Neural Attentive Circuits
Intra-agent speech permits zero-shot task acquisition
MACK: Multimodal Aligned Conceptual Knowledge for Unpaired Image-text Matching
Robustness to Label Noise Depends on the Shape of the Noise Distribution
A Theoretical Study on Solving Continual Learning
Anytime-Valid Inference For Multinomial Count Data
Scalable and Efficient Non-adaptive Deterministic Group Testing
Hierarchical Agglomerative Graph Clustering in Poly-Logarithmic Depth
Variable-rate hierarchical CPC leads to acoustic unit discovery in speech
SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression
Contextual Dynamic Pricing with Unknown Noise: Explore-then-UCB Strategy and Improved Regrets
Distributed Online Convex Optimization with Compressed Communication
GlanceNets: Interpretable, Leak-proof Concept-based Models
BEER: Fast $O(1/T)$ Rate for Decentralized Nonconvex Optimization with Communication Compression
On the Effectiveness of Persistent Homology
The Effects of Regularization and Data Augmentation are Class Dependent
On the Stability and Scalability of Node Perturbation Learning
Trimmed Maximum Likelihood Estimation for Robust Generalized Linear Model
Benefits of Additive Noise in Composing Classes with Bounded Capacity
EZNAS: Evolving Zero-Cost Proxies For Neural Architecture Scoring
Proppo: a Message Passing Framework for Customizable and Composable Learning Algorithms
Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees
Tempo: Accelerating Transformer-Based Model Training through Memory Footprint Reduction
CS-Shapley: Class-wise Shapley Values for Data Valuation in Classification
A New Family of Generalization Bounds Using Samplewise Evaluated CMI
Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations
On the Adversarial Robustness of Mixture of Experts
Graph Neural Networks are Dynamic Programmers
K-LITE: Learning Transferable Visual Models with External Knowledge
Mesoscopic modeling of hidden spiking neurons
Self-Supervised Learning Through Efference Copies
Self-Explaining Deviations for Coordination
Multi-Objective Deep Learning with Adaptive Reference Vectors
Overparameterization from Computational Constraints
AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning
Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning
On the Generalization Power of the Overfitted Three-Layer Neural Tangent Kernel Model
Provably Adversarially Robust Detection of Out-of-Distribution Data (Almost) for Free
Modular Flows: Differential Molecular Generation
Bridging Central and Local Differential Privacy in Data Acquisition Mechanisms
PAC Prediction Sets for Meta-Learning
Diffusion Models as Plug-and-Play Priors
MorphTE: Injecting Morphology in Tensorized Embeddings
Trajectory balance: Improved credit assignment in GFlowNets
On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond
Task-Free Continual Learning via Online Discrepancy Distance Learning
Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams
Evaluation beyond Task Performance: Analyzing Concepts in AlphaZero in Hex
Benchopt: Reproducible, efficient and collaborative optimization benchmarks
RNNs of RNNs: Recursive Construction of Stable Assemblies of Recurrent Neural Networks
Nonparametric Uncertainty Quantification for Single Deterministic Neural Network
Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints
Discovering and Overcoming Limitations of Noise-engineered Data-free Knowledge Distillation
Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation
SQ Lower Bounds for Learning Single Neurons with Massart Noise
Meta-Reward-Net: Implicitly Differentiable Reward Learning for Preference-based Reinforcement Learning
Average Sensitivity of Euclidean k-Clustering
A theory of weight distribution-constrained learning
Data augmentation for efficient learning from parametric experts
Active Bayesian Causal Inference
Template based Graph Neural Network with Optimal Transport Distances
Outlier-Robust Sparse Estimation via Non-Convex Optimization
Toward Understanding Privileged Features Distillation in Learning-to-Rank
The Neural Covariance SDE: Shaped Infinite Depth-and-Width Networks at Initialization
FP8 Quantization: The Power of the Exponent
Maximizing Revenue under Market Shrinkage and Market Uncertainty
UnfoldML: Cost-Aware and Uncertainty-Based Dynamic 2D Prediction for Multi-Stage Classification
Structural Analysis of Branch-and-Cut and the Learnability of Gomory Mixed Integer Cuts
DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning
Structure-Aware Image Segmentation with Homotopy Warping
Deep Learning Methods for Proximal Inference via Maximum Moment Restriction
On global convergence of ResNets: From finite to infinite width using linear parameterization
Residual Multiplicative Filter Networks for Multiscale Reconstruction
Reinforcement Learning with Non-Exponential Discounting
Towards Trustworthy Automatic Diagnosis Systems by Emulating Doctors' Reasoning with Deep Reinforcement Learning
On the symmetries of the synchronization problem in Cryo-EM: Multi-Frequency Vector Diffusion Maps on the Projective Plane
A Theoretical View on Sparsely Activated Networks
Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors
Implications of Model Indeterminacy for Explanations of Automated Decisions
NOMAD: Nonlinear Manifold Decoders for Operator Learning
Characterizing the Ventral Visual Stream with Response-Optimized Neural Encoding Models
How Sampling Impacts the Robustness of Stochastic Neural Networks
Forward-Backward Latent State Inference for Hidden Continuous-Time semi-Markov Chains
Shape And Structure Preserving Differential Privacy
On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning
Dynamic Pricing with Monotonicity Constraint under Unknown Parametric Demand Model
Cross-Linked Unified Embedding for cross-modality representation learning
Active Ranking without Strong Stochastic Transitivity
ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model
The Mechanism of Prediction Head in Non-contrastive Self-supervised Learning
Task Discovery: Finding the Tasks that Neural Networks Generalize on
Chaotic Regularization and Heavy-Tailed Limits for Deterministic Gradient Descent
LOT: Layer-wise Orthogonal Training on Improving l2 Certified Robustness
Few-Shot Fast-Adaptive Anomaly Detection
Learning dynamics of deep linear networks with multiple pathways
Turbocharging Solution Concepts: Solving NEs, CEs and CCEs with Neural Equilibrium Solvers
Multi-fidelity Monte Carlo: a pseudo-marginal approach
Learning sparse features can lead to overfitting in neural networks
Pushing the limits of fairness impossibility: Who's the fairest of them all?
Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions
Zonotope Domains for Lagrangian Neural Network Verification
Safety Guarantees for Neural Network Dynamic Systems via Stochastic Barrier Functions
Online Bipartite Matching with Advice: Tight Robustness-Consistency Tradeoffs for the Two-Stage Model
Improving Multi-Task Generalization via Regularizing Spurious Correlation
Operative dimensions in unconstrained connectivity of recurrent neural networks
Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules
Collaborative Linear Bandits with Adversarial Agents: Near-Optimal Regret Bounds
Generating Training Data with Language Models: Towards Zero-Shot Language Understanding
Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank
Towards Practical Few-shot Query Sets: Transductive Minimum Description Length Inference
Randomized Channel Shuffling: Minimal-Overhead Backdoor Attack Detection without Clean Datasets
MAgNet: Mesh Agnostic Neural PDE Solver
Online Learning and Pricing for Network Revenue Management with Reusable Resources
Learning Modular Simulations for Homogeneous Systems
Instability and Local Minima in GAN Training with Kernel Discriminators
On Computing Probabilistic Explanations for Decision Trees
Distributed Optimization for Overparameterized Problems: Achieving Optimal Dimension Independent Communication Complexity
Lost in Latent Space: Examining failures of disentangled models at combinatorial generalisation
When Combinatorial Thompson Sampling meets Approximation Regret
Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models
Detecting Abrupt Changes in Sequential Pairwise Comparison Data
Sparse Fourier Backpropagation in Cryo-EM Reconstruction
When Does Differentially Private Learning Not Suffer in High Dimensions?
A Fast Scale-Invariant Algorithm for Non-negative Least Squares with Non-negative Data
(Optimal) Online Bipartite Matching with Degree Information
Learning from a Sample in Online Algorithms
Data-Driven Conditional Robust Optimization
Linear Label Ranking with Bounded Noise
Estimation of Entropy in Constant Space with Improved Sample Complexity
Escaping from the Barren Plateau via Gaussian Initializations in Deep Variational Quantum Circuits
Expected Frequency Matrices of Elections: Computation, Geometry, and Preference Learning
Robust Neural Posterior Estimation and Statistical Model Criticism
CryptoGCN: Fast and Scalable Homomorphically Encrypted Graph Convolutional Network Inference
The Missing Invariance Principle found -- the Reciprocal Twin of Invariant Risk Minimization
MABSplit: Faster Forest Training Using Multi-Armed Bandits
Marksman Backdoor: Backdoor Attacks with Arbitrary Target Class
Collaborative Learning of Discrete Distributions under Heterogeneity and Communication Constraints
Sample-Efficient Reinforcement Learning of Partially Observable Markov Games
Phase transitions in when feedback is useful
The Role of Baselines in Policy Gradient Optimization
Autoformalization with Large Language Models
Differentially Private Generalized Linear Models Revisited
Learning to Follow Instructions in Text-Based Games
On Learning and Refutation in Noninteractive Local Differential Privacy
Cryptographic Hardness of Learning Halfspaces with Massart Noise
Instance-optimal PAC Algorithms for Contextual Bandits
Do Current Multi-Task Optimization Methods in Deep Learning Even Help?
Prompt Certified Machine Unlearning with Randomized Gradient Smoothing and Quantization
Unsupervised Reinforcement Learning with Contrastive Intrinsic Control
Exact learning dynamics of deep linear networks with prior knowledge
Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency
Regret Bounds for Multilabel Classification in Sparse Label Regimes
Characterizing Datapoints via Second-Split Forgetting
Training language models to follow instructions with human feedback
S4ND: Modeling Images and Videos as Multidimensional Signals with State Spaces
Defining and Characterizing Reward Gaming
Adversarial training for high-stakes reliability
Semantic Probabilistic Layers for Neuro-Symbolic Learning
WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents
Maximizing and Satisficing in Multi-armed Bandits with Graph Information
Learning Probabilistic Models from Generator Latent Spaces with Hat EBM
Spherical Channels for Modeling Atomic Interactions
HyperTree Proof Search for Neural Theorem Proving
Exploring the Latent Space of Autoencoders with Interventional Assays
Root Cause Analysis of Failures in Microservices through Causal Discovery
Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks
Finite-Sample Maximum Likelihood Estimation of Location
BayesPCN: A Continually Learnable Predictive Coding Associative Memory
On the detrimental effect of invariances in the likelihood for variational inference
Learning to Compare Nodes in Branch and Bound with Graph Neural Networks
Parameter-free Regret in High Probability with Heavy Tails
Multi-Game Decision Transformers
Structural Pruning via Latency-Saliency Knapsack
The Query Complexity of Cake Cutting
Best of Both Worlds Model Selection
Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation
New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound
Memory safe computations with XLA compiler
Learning NP-Hard Multi-Agent Assignment Planning using GNN: Inference on a Random Graph and Provable Auction-Fitted Q-learning
Fairness in Federated Learning via Core-Stability
Accelerating Certified Robustness Training via Knowledge Transfer
Certifying Some Distributional Fairness with Subpopulation Decomposition
A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation
Sublinear Algorithms for Hierarchical Clustering
A Deep Reinforcement Learning Framework for Column Generation
Logical Activation Functions: Logit-space equivalents of Probabilistic Boolean Operators
EAGER: Asking and Answering Questions for Automatic Reward Shaping in Language-guided RL
End-to-end Stochastic Optimization with Energy-based Model
ReCo: Retrieve and Co-segment for Zero-shot Transfer
Human-Robotic Prosthesis as Collaborating Agents for Symmetrical Walking
Adaptive Interest for Emphatic Reinforcement Learning
Chaotic Dynamics are Intrinsic to Neural Network Training with SGD
Local Bayesian optimization via maximizing probability of descent
Learning the Structure of Large Networked Systems Obeying Conservation Laws
Near-Optimal No-Regret Learning Dynamics for General Convex Games
The Impact of Task Underspecification in Evaluating Deep Reinforcement Learning
A Practical, Progressively-Expressive GNN
ELIGN: Expectation Alignment as a Multi-Agent Intrinsic Reward
Provably tuning the ElasticNet across instances
Fast Neural Kernel Embeddings for General Activations
Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution Shifts
Simple and Optimal Greedy Online Contention Resolution Schemes
Modeling Transitivity and Cyclicity in Directed Graphs via Binary Code Box Embeddings
Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction
Decoupled Context Processing for Context Augmented Language Modeling
Efficiency Ordering of Stochastic Gradient Descent
Robust Streaming PCA
Learning Partial Equivariances From Data
[Re] Lifting 2D StyleGAN for 3D-Aware Face Generation
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting
Unsupervised Causal Generative Understanding of Images
Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptation
Fair Ranking with Noisy Protected Attributes
Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models
Zero-Sum Stochastic Stackelberg Games
Can Hybrid Geometric Scattering Networks Help Solve the Maximum Clique Problem?
NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric Photonic Device Simulation
Mining Multi-Label Samples from Single Positive Labels
Why So Pessimistic? Estimating Uncertainties for Offline RL through Ensembles, and Why Their Independence Matters
Efficient Phi-Regret Minimization in Extensive-Form Games via Online Mirror Descent
Exponential Family Model-Based Reinforcement Learning via Score Matching
Object Scene Representation Transformer
Geometric Order Learning for Rank Estimation
Learning with convolution and pooling operations in kernel methods
Dataset Distillation using Neural Feature Regression
Influencing Long-Term Behavior in Multiagent Reinforcement Learning
Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization Algorithm
Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search
Learning Contrastive Embedding in Low-Dimensional Space
Exploring Example Influence in Continual Learning
JAWS: Auditing Predictive Uncertainty Under Covariate Shift
One for All: Simultaneous Metric and Preference Learning over Multiple Users
Paraphrasing Is All You Need for Novel Object Captioning
Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative
Multiview Human Body Reconstruction from Uncalibrated Cameras
FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning
Empirical Gateaux Derivatives for Causal Inference
AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generators
Benefits of Permutation-Equivariance in Auction Mechanisms
Learning Active Camera for Multi-Object Navigation
Toward Efficient Robust Training against Union of $\ell_p$ Threat Models
Mask Matching Transformer for Few-Shot Segmentation
A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs
Symplectic Spectrum Gaussian Processes: Learning Hamiltonians from Noisy and Sparse Data
GREED: A Neural Framework for Learning Graph Distance Functions
Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty
Consistent Sufficient Explanations and Minimal Local Rules for explaining the decision of any classifier or regressor
DaDA: Distortion-aware Domain Adaptation for Unsupervised Semantic Segmentation
Learning Optical Flow from Continuous Spike Streams
Retrospective Adversarial Replay for Continual Learning
Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach
On Feature Learning in the Presence of Spurious Correlations
Explaining Preferences with Shapley Values
Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean Estimation
ReFactor GNNs: Revisiting Factorisation-based Models from a Message-Passing Perspective
Block-Recurrent Transformers
Hamiltonian Latent Operators for content and motion disentanglement in image sequences
Learning (Very) Simple Generative Models Is Hard
Understanding the Generalization Benefit of Normalization Layers: Sharpness Reduction
A Non-asymptotic Analysis of Non-parametric Temporal-Difference Learning
Rethinking Generalization in Few-Shot Classification
VectorAdam for Rotation Equivariant Geometry Optimization
Keypoint-Guided Optimal Transport with Applications in Heterogeneous Domain Adaptation
Supervising the Multi-Fidelity Race of Hyperparameter Configurations
Trajectory of Mini-Batch Momentum: Batch Size Saturation and Convergence in High Dimensions
Single Model Uncertainty Estimation via Stochastic Data Centering
CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers
Graph Neural Networks with Adaptive Readouts
Adaptive Distribution Calibration for Few-Shot Learning with Hierarchical Optimal Transport
Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation
Beyond Mahalanobis Distance for Textual OOD Detection
Tensor Program Optimization with Probabilistic Programs
VICE: Variational Interpretable Concept Embeddings
Learning single-index models with shallow neural networks
Near-Optimal Randomized Exploration for Tabular Markov Decision Processes
Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective
LOG: Active Model Adaptation for Label-Efficient OOD Generalization
Structural Knowledge Distillation for Object Detection
Semantic uncertainty intervals for disentangled latent spaces
Uni[MASK]: Unified Inference in Sequential Decision Problems
Bayesian Optimistic Optimization: Optimistic Exploration for Model-based Reinforcement Learning
Invertible Monotone Operators for Normalizing Flows
A Transformer-Based Object Detector with Coarse-Fine Crossing Representations
Distinguishing Learning Rules with Brain Machine Interfaces
Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning
Expected Improvement for Contextual Bandits
BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework
Graph Neural Network Bandits
Mean Estimation with User-level Privacy under Data Heterogeneity
Precise Regret Bounds for Log-loss via a Truncated Bayesian Algorithm
ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpoints
LobsDICE: Offline Learning from Observation via Stationary Distribution Correction Estimation
360-MLC: Multi-view Layout Consistency for Self-training and Hyper-parameter Tuning
Reduced Representation of Deformation Fields for Effective Non-rigid Shape Matching
Explain My Surprise: Learning Efficient Long-Term Memory by predicting uncertain outcomes
A Simple and Optimal Policy Design for Online Learning with Safety against Heavy-tailed Risk
Policy Optimization with Linear Temporal Logic Constraints
Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning
Decomposed Knowledge Distillation for Class-Incremental Semantic Segmentation
On the Theoretical Properties of Noise Correlation in Stochastic Optimization
NCP: Neural Correspondence Prior for Effective Unsupervised Shape Matching
ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs
On Divergence Measures for Bayesian Pseudocoresets
Alleviating the Sample Selection Bias in Few-shot Learning by Removing Projection to the Centroid
Can Push-forward Generative Models Fit Multimodal Distributions?
Posterior and Computational Uncertainty in Gaussian Processes
MORA: Improving Ensemble Robustness Evaluation with Model Reweighing Attack
Learning to Sample and Aggregate: Few-shot Reasoning over Temporal Knowledge Graphs
Advancing Model Pruning via Bi-level Optimization
An Algorithm for Learning Switched Linear Dynamics from Data
Batch Bayesian optimisation via density-ratio estimation with guarantees
An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects
Structuring Uncertainty for Fine-Grained Sampling in Stochastic Segmentation Networks
Multi-objective Deep Data Generation with Correlated Property Control
Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations
Learning Debiased Classifier with Biased Committee
Surprising Instabilities in Training Deep Networks and a Theoretical Analysis
Capturing Failures of Large Language Models via Human Cognitive Biases
Nonnegative Tensor Completion via Integer Optimization
Equivariant Networks for Crystal Structures
LieGG: Studying Learned Lie Group Generators
On-Demand Sampling: Learning Optimally from Multiple Distributions
A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning
Robust Model Selection and Nearly-Proper Learning for GMMs
Combining Explicit and Implicit Regularization for Efficient Learning in Deep Networks
A Unified Framework for Alternating Offline Model Training and Policy Learning
Automatic Differentiation of Programs with Discrete Randomness
Thinned random measures for sparse graphs with overlapping communities
Hybrid Neural Autoencoders for Stimulus Encoding in Visual and Other Sensory Neuroprostheses
Tight Analysis of Extra-gradient and Optimistic Gradient Methods For Nonconvex Minimax Problems
Amortized Inference for Causal Structure Learning
Staircase Attention for Recurrent Processing of Sequences
A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs
Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off
Biologically plausible solutions for spiking networks with efficient coding
Algorithms with Prediction Portfolios
SAGDA: Achieving $\mathcal{O}(\epsilon^{-2})$ Communication Complexity in Federated Min-Max Learning
Deep Compression of Pre-trained Transformer Models
Beyond neural scaling laws: beating power law scaling via data pruning
Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation
Learning Optimal Flows for Non-Equilibrium Importance Sampling
Incentivizing Combinatorial Bandit Exploration
A Simple Decentralized Cross-Entropy Method
Neural Abstractions
Learning Dense Object Descriptors from Multiple Views for Low-shot Category Generalization
Flowification: Everything is a normalizing flow
Non-monotonic Resource Utilization in the Bandits with Knapsacks Problem
Evaluating Robustness to Dataset Shift via Parametric Robustness Sets
Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement
Private and Communication-Efficient Algorithms for Entropy Estimation
Kernel Multimodal Continuous Attention
Stars: Tera-Scale Graph Building for Clustering and Learning
Anonymous Bandits for Multi-User Systems
Understanding Deep Contrastive Learning via Coordinate-wise Optimization
Stochastic Halpern Iteration with Variance Reduction for Stochastic Monotone Inclusions
PALMER: Perception - Action Loop with Memory for Long-Horizon Planning
Curious Exploration via Structured World Models Yields Zero-Shot Object Manipulation
Finite Sample Analysis Of Dynamic Regression Parameter Learning
Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization
Improved Coresets for Euclidean $k$-Means
Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data
Distributed Influence-Augmented Local Simulators for Parallel MARL in Large Networked Systems
Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs
Grounded Video Situation Recognition
Learning to Scaffold: Optimizing Model Explanations for Teaching
Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social Text Classification
Efficient Methods for Non-stationary Online Learning
Sustainable Online Reinforcement Learning for Auto-bidding
Effectiveness of Vision Transformer for Fast and Accurate Single-Stage Pedestrian Detection
On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models
Isometric 3D Adversarial Examples in the Physical World
The Hessian Screening Rule
Measuring Data Reconstruction Defenses in Collaborative Inference Systems
A Stochastic Linearized Augmented Lagrangian Method for Decentralized Bilevel Optimization
Kernel Memory Networks: A Unifying Framework for Memory Modeling
A Neural Pre-Conditioning Active Learning Algorithm to Reduce Label Complexity
Flexible Diffusion Modeling of Long Videos
Learning Structure from the Ground up---Hierarchical Representation Learning by Chunking
Meta-Complementing the Semantics of Short Texts in Neural Topic Models
Robust Feature-Level Adversaries are Interpretability Tools
Knowledge-Aware Bayesian Deep Topic Model
GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Games
Quantum Algorithms for Sampling Log-Concave Distributions and Estimating Normalizing Constants
Nearly Optimal Best-of-Both-Worlds Algorithms for Online Learning with Feedback Graphs
FourierNets enable the design of highly non-local optical encoders for computational imaging
TVLT: Textless Vision-Language Transformer
No Free Lunch from Deep Learning in Neuroscience: A Case Study through Models of the Entorhinal-Hippocampal Circuit
Retaining Knowledge for Learning with Dynamic Definition
XTC: Extreme Compression for Pre-trained Transformers Made Simple and Efficient
PAC: Assisted Value Factorization with Counterfactual Predictions in Multi-Agent Reinforcement Learning
Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts
Compositional Generalization in Unsupervised Compositional Representation Learning: A Study on Disentanglement and Emergent Language
Fairness without Demographics through Knowledge Distillation
Deep Bidirectional Language-Knowledge Graph Pretraining
Rethinking Value Function Learning for Generalization in Reinforcement Learning
Instance-Dependent Near-Optimal Policy Identification in Linear MDPs via Online Experiment Design
Exposing and Exploiting Fine-Grained Block Structures for Fast and Accurate Sparse Training
Parameters or Privacy: A Provable Tradeoff Between Overparameterization and Membership Inference
Efficient Dataset Distillation using Random Feature Approximation
Locally Hierarchical Auto-Regressive Modeling for Image Generation
Interaction-Grounded Learning with Action-Inclusive Feedback
AdaFocal: Calibration-aware Adaptive Focal Loss
Convergence for score-based generative modeling with polynomial complexity
Toward Robust Spiking Neural Network Against Adversarial Perturbation
Layer Freezing & Data Sieving: Missing Pieces of a Generic Framework for Sparse Training
Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation
$\alpha$-ReQ : Assessing Representation Quality in Self-Supervised Learning by measuring eigenspectrum decay
Bounded-Regret MPC via Perturbation Analysis: Prediction Error, Constraints, and Nonlinearity
NaturalProver: Grounded Mathematical Proof Generation with Language Models
Predictive Querying for Autoregressive Neural Sequence Models
Differentially Private Linear Sketches: Efficient Implementations and Applications
Probable Domain Generalization via Quantile Risk Minimization
Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantification
Minimax Optimal Online Imitation Learning via Replay Estimation
Subspace Recovery from Heterogeneous Data with Non-isotropic Noise
Transferring Fairness under Distribution Shifts via Fair Consistency Regularization
Exploring the Whole Rashomon Set of Sparse Decision Trees
On Image Segmentation With Noisy Labels: Characterization and Volume Properties of the Optimal Solutions to Accuracy and Dice
AutoML Two-Sample Test
Efficient Scheduling of Data Augmentation for Deep Reinforcement Learning
Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations
Sampling from Log-Concave Distributions with Infinity-Distance Guarantees
Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks
Distributional Reinforcement Learning for Risk-Sensitive Policies
Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis
Promising or Elusive? Unsupervised Object Segmentation from Real-world Single Images
Unsupervised Domain Adaptation for Semantic Segmentation using Depth Distribution
Data-Efficient Structured Pruning via Submodular Optimization
Structured Energy Network As a Loss
Iso-Dream: Isolating and Leveraging Noncontrollable Visual Dynamics in World Models
Improving Barely Supervised Learning by Discriminating Unlabeled Samples with Super-Class
Rethinking and Improving Robustness of Convolutional Neural Networks: a Shapley Value-based Approach in Frequency Domain
Exploring evolution-aware & -free protein language models as protein function predictors
Boosting the Performance of Generic Deep Neural Network Frameworks with Log-supermodular CRFs
On the Tradeoff Between Robustness and Fairness
Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean Measures
Causality-driven Hierarchical Structure Discovery for Reinforcement Learning
Are AlphaZero-like Agents Robust to Adversarial Perturbations?
Thinking Outside the Ball: Optimal Learning with Gradient Descent for Generalized Linear Stochastic Convex Optimization
Pluralistic Image Completion with Gaussian Mixture Models
Generalization Analysis on Learning with a Concurrent Verifier
Receding Horizon Inverse Reinforcement Learning
Learning to Share in Networked Multi-Agent Reinforcement Learning
FIRE: Semantic Field of Words Represented as Non-Linear Functions
Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loop
ProtoX: Explaining a Reinforcement Learning Agent via Prototyping
Pyramid Attention For Source Code Summarization
Taming Fat-Tailed (“Heavier-Tailed” with Potentially Infinite Variance) Noise in Federated Learning
Maximum a posteriori natural scene reconstruction from retinal ganglion cells with deep denoiser priors
DigGAN: Discriminator gradIent Gap Regularization for GAN Training with Limited Data
DNA: Proximal Policy Optimization with a Dual Network Architecture
Will Bilevel Optimizers Benefit from Loops
Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoders
Redeeming intrinsic rewards via constrained optimization
Target alignment in truncated kernel ridge regression
Queue Up Your Regrets: Achieving the Dynamic Capacity Region of Multiplayer Bandits
Mismatched No More: Joint Model-Policy Optimization for Model-Based RL
Dynamic Sparse Network for Time Series Classification: Learning What to “See”
Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Functions
Constrained Predictive Coding as a Biologically Plausible Model of the Cortical Hierarchy
Perturbation Learning Based Anomaly Detection
Hierarchical Graph Transformer with Adaptive Node Sampling
LogiGAN: Learning Logical Reasoning via Adversarial Pre-training
Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs
Structure-Preserving 3D Garment Modeling with Neural Sewing Machines
Improved Bounds on Neural Complexity for Representing Piecewise Linear Functions
On the Limitations of Stochastic Pre-processing Defenses
ResQ: A Residual Q Function-based Approach for Multi-Agent Reinforcement Learning Value Factorization
Global Convergence and Stability of Stochastic Gradient Descent
Delving into Out-of-Distribution Detection with Vision-Language Representations
Recruitment Strategies That Take a Chance
Inference and Sampling for Archimax Copulas
Text Classification with Born's Rule
Cluster and Aggregate: Face Recognition with Large Probe Set
VTC-LFC: Vision Transformer Compression with Low-Frequency Components
Lipschitz Bandits with Batched Feedback
Formulating Robustness Against Unforeseen Attacks
Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks
Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap
CageNeRF: Cage-based Neural Radiance Field for Generalized 3D Deformation and Animation
Sparse2Dense: Learning to Densify 3D Features for 3D Object Detection
Non-Gaussian Tensor Programs
Understanding the Eluder Dimension
Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization
Local Linear Convergence of Gradient Methods for Subspace Optimization via Strict Complementarity
Simulation-guided Beam Search for Neural Combinatorial Optimization
Quo Vadis: Is Trajectory Forecasting the Key Towards Long-Term Multi-Object Tracking?
Meta-Reinforcement Learning with Self-Modifying Networks
Respecting Transfer Gap in Knowledge Distillation
What is Where by Looking: Weakly-Supervised Open-World Phrase-Grounding without Text Inputs
TarGF: Learning Target Gradient Field to Rearrange Objects without Explicit Goal Specification
One-Inlier is First: Towards Efficient Position Encoding for Point Cloud Registration
I2DFormer: Learning Image to Document Attention for Zero-Shot Image Classification
Sharing Knowledge for Meta-learning with Feature Descriptions
Large-batch Optimization for Dense Visual Predictions: Training Faster R-CNN in 4.2 Minutes
Continual Learning with Evolving Class Ontologies
Quasi-Newton Methods for Saddle Point Problems
TANGO: Text-driven Photorealistic and Robust 3D Stylization via Lighting Decomposition
Asymptotic Properties for Bayesian Neural Network in Besov Space
Planning for Sample Efficient Imitation Learning
Peripheral Vision Transformer
Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization
HSDF: Hybrid Sign and Distance Field for Modeling Surfaces with Arbitrary Topologies
Approximate Secular Equations for the Cubic Regularization Subproblem
Faster Stochastic Algorithms for Minimax Optimization under Polyak-{\L}ojasiewicz Condition
Unsupervised Learning of Equivariant Structure from Sequences
Inception Transformer
Signal Recovery with Non-Expansive Generative Network Priors
Counterfactual harm
Posterior Collapse of a Linear Latent Variable Model
Harmonizing the object recognition strategies of deep neural networks with humans
When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment
Exploit Reward Shifting in Value-Based Deep-RL: Optimistic Curiosity-Based Exploration and Conservative Exploitation via Linear Reward Shaping
Model-Based Imitation Learning for Urban Driving
OnePose++: Keypoint-Free One-Shot Object Pose Estimation without CAD Models
ELIAS: End-to-End Learning to Index and Search in Large Output Spaces
QUARK: Controllable Text Generation with Reinforced Unlearning
Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition
Finding Differences Between Transformers and ConvNets Using Counterfactual Simulation Testing
Anticipating Performativity by Predicting from Predictions
Fast Vision Transformers with HiLo Attention
OpenAUC: Towards AUC-Oriented Open-Set Recognition
Exploring the Algorithm-Dependent Generalization of AUPRC Optimization with List Stability
Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models
Differentiable Analog Quantum Computing for Optimization and Control
Losses Can Be Blessings: Routing Self-Supervised Speech Representations Towards Efficient Multilingual and Multitask Speech Processing
Monte Carlo Tree Descent for Black-Box Optimization
On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgetting
Robust Imitation of a Few Demonstrations with a Backwards Model
AMP: Automatically Finding Model Parallel Strategies with Heterogeneity Awareness
Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with an Inexact Prox
Performative Power
SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery
Benign, Tempered, or Catastrophic: Toward a Refined Taxonomy of Overfitting
The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift
Confident Approximate Policy Iteration for Efficient Local Planning in $q^\pi$-realizable MDPs
Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity
Society of Agents: Regret Bounds of Concurrent Thompson Sampling
Exploring Length Generalization in Large Language Models
Unsupervised Learning for Combinatorial Optimization with Principled Objective Relaxation
GPT3.int8(): 8-bit Matrix Multiplication for Transformers at Scale
Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks
Revisiting Sparse Convolutional Model for Visual Recognition
Temporal Latent Bottleneck: Synthesis of Fast and Slow Processing Mechanisms in Sequence Learning
MoCoDA: Model-based Counterfactual Data Augmentation
Beyond Adult and COMPAS: Fair Multi-Class Prediction via Information Projection
On the generalization of learning algorithms that do not converge
Capturing Graphs with Hypo-Elliptic Diffusions
Hypothesis Testing for Differentially Private Linear Regression
Recurrent Convolutional Neural Networks Learn Succinct Learning Algorithms
AutoST: Towards the Universal Modeling of Spatio-temporal Sequences
SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning
ESCADA: Efficient Safety and Context Aware Dose Allocation for Precision Medicine
Explicit Tradeoffs between Adversarial and Natural Distributional Robustness
Generalization Bounds for Gradient Methods via Discrete and Continuous Prior
CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification
BYOL-Explore: Exploration by Bootstrapped Prediction
Ordered Subgraph Aggregation Networks
Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability
Label Noise in Adversarial Training: A Novel Perspective to Study Robust Overfitting
Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime
Text-Adaptive Multiple Visual Prototype Matching for Video-Text Retrieval
An In-depth Study of Stochastic Backpropagation
Tractable Optimality in Episodic Latent MABs
Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning
Improving Certified Robustness via Statistical Learning with Logical Reasoning
Online Decision Mediation
Deep Differentiable Logic Gate Networks
Double Bubble, Toil and Trouble: Enhancing Certified Robustness through Transitivity
Associating Objects and Their Effects in Video through Coordination Games
Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits
Precise Learning Curves and Higher-Order Scalings for Dot-product Kernel Regression
Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective Inference
Multi-block Min-max Bilevel Optimization with Applications in Multi-task Deep AUC Maximization
Agreement-on-the-line: Predicting the Performance of Neural Networks under Distribution Shift
Neural Conservation Laws: A Divergence-Free Perspective
Sparse Hypergraph Community Detection Thresholds in Stochastic Block Model
Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries
Latent Hierarchical Causal Structure Discovery with Rank Constraints
Task-Agnostic Graph Explanations
ZSON: Zero-Shot Object-Goal Navigation using Multimodal Goal Embeddings
Towards Optimal Communication Complexity in Distributed Non-Convex Optimization
Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement
Optimal Rates for Regularized Conditional Mean Embedding Learning
Are All Losses Created Equal: A Neural Collapse Perspective
Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees
What You See is What You Get: Principled Deep Learning via Distributional Generalization
Knowledge Distillation: Bad Models Can Be Good Role Models
Fine-Tuning Pre-Trained Language Models Effectively by Optimizing Subnetworks Adaptively
Lifelong Neural Predictive Coding: Learning Cumulatively Online without Forgetting
Rare Gems: Finding Lottery Tickets at Initialization
Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit
Neural Approximation of Graph Topological Features
Near-Optimal Regret Bounds for Multi-batch Reinforcement Learning
Surprise Minimizing Multi-Agent Learning with Energy-based Models
Sparse Structure Search for Delta Tuning
Stability and Generalization for Markov Chain Stochastic Gradient Methods
Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare
Discovery of Single Independent Latent Variable
MoGDE: Boosting Mobile Monocular 3D Object Detection with Ground Depth Estimation
Compressible-composable NeRF via Rank-residual Decomposition
Asymmetric Temperature Scaling Makes Larger Networks Teach Well Again
DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps
Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEs
Neural Shape Deformation Priors
Hierarchical Channel-spatial Encoding for Communication-efficient Collaborative Learning
Debugging and Explaining Metric Learning Approaches: An Influence Function Based Perspective
Factuality Enhanced Language Models for Open-Ended Text Generation
Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets
A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits
MaskTune: Mitigating Spurious Correlations by Forcing to Explore
Scalable Sensitivity and Uncertainty Analyses for Causal-Effect Estimates of Continuous-Valued Interventions
Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathology
Reconstructing Training Data From Trained Neural Networks
Use-Case-Grounded Simulations for Explanation Evaluation
Differentiable hierarchical and surrogate gradient search for spiking neural networks
CalFAT: Calibrated Federated Adversarial Training with Label Skewness
Cluster Randomized Designs for One-Sided Bipartite Experiments
Multi-Sample Training for Neural Image Compression
On the Parameterization and Initialization of Diagonal State Space Models
Solving Quantitative Reasoning Problems with Language Models
Learnable Polyphase Sampling for Shift Invariant and Equivariant Convolutional Networks
D^2NeRF: Self-Supervised Decoupling of Dynamic and Static Objects from a Monocular Video
Semi-Supervised Video Salient Object Detection Based on Uncertainty-Guided Pseudo Labels
C2FAR: Coarse-to-Fine Autoregressive Networks for Precise Probabilistic Forecasting
SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks
Squeezeformer: An Efficient Transformer for Automatic Speech Recognition
Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks
Generalizing Bayesian Optimization with Decision-theoretic Entropies
Dict-TTS: Learning to Pronounce with Prior Dictionary Knowledge for Text-to-Speech
The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes
Unsupervised Object Detection Pretraining with Joint Object Priors Generation and Detector Learning
Learning Chaotic Dynamics in Dissipative Systems
MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction
SeqPATE: Differentially Private Text Generation via Knowledge Distillation
DENSE: Data-Free One-Shot Federated Learning
Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization
Is $L^2$ Physics Informed Loss Always Suitable for Training Physics Informed Neural Network?
Hiding Images in Deep Probabilistic Models
Factored Adaptation for Non-Stationary Reinforcement Learning
Optimal Algorithms for Decentralized Stochastic Variational Inequalities
Semi-supervised Vision Transformers at Scale
Deep Model Reassembly
Your Transformer May Not be as Powerful as You Expect
InsPro: Propagating Instance Query and Proposal for Online Video Instance Segmentation
Linear tree shap
Delving into Sequential Patches for Deepfake Detection
Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protection
ClimbQ: Class Imbalanced Quantization Enabling Robustness on Efficient Inferences
Learning Latent Seasonal-Trend Representations for Time Series Forecasting
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks
Back Razor: Memory-Efficient Transfer Learning by Self-Sparsified Backpropagation
DreamShard: Generalizable Embedding Table Placement for Recommender Systems
Dataset Distillation via Factorization
Video Diffusion Models
Theseus: A Library for Differentiable Nonlinear Optimization
Decoupling Features in Hierarchical Propagation for Video Object Segmentation
RankFeat: Rank-1 Feature Removal for Out-of-distribution Detection
Explainable Reinforcement Learning via Model Transforms
Matryoshka Representation Learning
VoxGRAF: Fast 3D-Aware Image Synthesis with Sparse Voxel Grids
Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation
MetaMask: Revisiting Dimensional Confounder for Self-Supervised Learning
LGDN: Language-Guided Denoising Network for Video-Language Modeling
PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining
Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning
Adapting Self-Supervised Vision Transformers by Probing Attention-Conditioned Masking Consistency
Flexible Neural Image Compression via Code Editing
Learning Physics Constrained Dynamics Using Autoencoders
Active Learning with Neural Networks: Insights from Nonparametric Statistics
Understanding Robust Learning through the Lens of Representation Similarities
Beyond Separability: Analyzing the Linear Transferability of Contrastive Representations to Related Subpopulations
Few-shot Task-agnostic Neural Architecture Search for Distilling Large Language Models
Zero-Shot Video Question Answering via Frozen Bidirectional Language Models
Outsourcing Training without Uploading Data via Efficient Collaborative Open-Source Sampling
Measuring and Reducing Model Update Regression in Structured Prediction for NLP
Coordinates Are NOT Lonely - Codebook Prior Helps Implicit Neural 3D representations
Multitasking Models are Robust to Structural Failure: A Neural Model for Bilingual Cognitive Reserve
A Policy-Guided Imitation Approach for Offline Reinforcement Learning
Asymptotics of smoothed Wasserstein distances in the small noise regime
Finite-Time Last-Iterate Convergence for Learning in Multi-Player Games
CARD: Classification and Regression Diffusion Models
GraphDE: A Generative Framework for Debiased Learning and Out-of-Distribution Detection on Graphs
Unlabelled Sample Compression Schemes for Intersection-Closed Classes and Extremal Classes
Concentration of Data Encoding in Parameterized Quantum Circuits
Learning Efficient Vision Transformers via Fine-Grained Manifold Distillation
M$^4$I: Multi-modal Models Membership Inference
Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules
VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts
Pre-Trained Language Models for Interactive Decision-Making
Learning from Label Proportions by Learning with Label Noise
A Closer Look at Offline RL Agents
Beyond spectral gap: the role of the topology in decentralized learning
A permutation-free kernel two-sample test
C-Mixup: Improving Generalization in Regression
Generalizing Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary Losses
Efficient Multi-agent Communication via Self-supervised Information Aggregation
EfficientFormer: Vision Transformers at MobileNet Speed
Pseudo-Riemannian Graph Convolutional Networks
Fast Algorithms for Packing Proportional Fairness and its Dual
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees
Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes
Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress
Active Exploration for Inverse Reinforcement Learning
UniGAN: Reducing Mode Collapse in GANs using a Uniform Generator
Diffusion Curvature for Estimating Local Curvature in High Dimensional Data
Batch-Size Independent Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms or Independent Arms
On Enforcing Better Conditioned Meta-Learning for Rapid Few-Shot Adaptation
Efficient learning of nonlinear prediction models with time-series privileged information
Training and Inference on Any-Order Autoregressive Models the Right Way
SPD: Synergy Pattern Diversifying Oriented Unsupervised Multi-agent Reinforcement Learning
GAPX: Generalized Autoregressive Paraphrase-Identification X
CATER: Intellectual Property Protection on Text Generation APIs via Conditional Watermarks
Reinforcement Learning with a Terminator
Bringing Image Scene Structure to Video via Frame-Clip Consistency of Object Tokens
Class-Dependent Label-Noise Learning with Cycle-Consistency Regularization
CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds
Sparse Interaction Additive Networks via Feature Interaction Detection and Sparse Selection
Object-Category Aware Reinforcement Learning
Decision-Focused Learning without Decision-Making: Learning Locally Optimized Decision Losses
Universally Expressive Communication in Multi-Agent Reinforcement Learning
Are GANs overkill for NLP?
Simple Mechanisms for Welfare Maximization in Rich Advertising Auctions
Scalable Interpretability via Polynomials
NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation
Learning Audio-Visual Dynamics Using Scene Graphs for Audio Source Separation
Symmetry Teleportation for Accelerated Optimization
The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning
Truncated proposals for scalable and hassle-free simulation-based inference
Large-Scale Retrieval for Reinforcement Learning
Decoupled Self-supervised Learning for Graphs
In Differential Privacy, There is Truth: on Vote-Histogram Leakage in Ensemble Private Learning
Handcrafted Backdoors in Deep Neural Networks
Structuring Representations Using Group Invariants
A sharp NMF result with applications in network modeling
Improving Policy Learning via Language Dynamics Distillation
Pure Transformers are Powerful Graph Learners
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification
Few-shot Image Generation via Adaptation-Aware Kernel Modulation
Towards Understanding Grokking: An Effective Theory of Representation Learning
Online Agnostic Multiclass Boosting
Adversarial Unlearning: Reducing Confidence Along Adversarial Directions
Robust Imitation via Mirror Descent Inverse Reinforcement Learning
HyperMiner: Topic Taxonomy Mining with Hyperbolic Embedding
Oracle-Efficient Online Learning for Smoothed Adversaries
Multiclass Learnability Beyond the PAC Framework: Universal Rates and Partial Concept Classes
Lower Bounds on Randomly Preconditioned Lasso via Robust Sparse Designs
Learning from Distributed Users in Contextual Linear Bandits Without Sharing the Context
Accelerating Sparse Convolution with Column Vector-Wise Sparsity
Fast Instrument Learning with Faster Rates
LTMD: Learning Improvement of Spiking Neural Networks with Learnable Thresholding Neurons and Moderate Dropout
Improving Neural Ordinary Differential Equations with Nesterov's Accelerated Gradient Method
Learning Neural Set Functions Under the Optimal Subset Oracle
Guaranteed Conservation of Momentum for Learning Particle-based Fluid Dynamics
Universality of Group Convolutional Neural Networks Based on Ridgelet Analysis on Groups
On the Spectral Bias of Convolutional Neural Tangent and Gaussian Process Kernels
Beyond L1: Faster and Better Sparse Models with skglm
Improving GANs with A Dynamic Discriminator
Streaming Radiance Fields for 3D Video Synthesis
On the non-universality of deep learning: quantifying the cost of symmetry
GraB: Finding Provably Better Data Permutations than Random Reshuffling
Enhancing Safe Exploration Using Safety State Augmentation
Robust Binary Models by Pruning Randomly-initialized Networks
Optimal and Adaptive Monteiro-Svaiter Acceleration
Reinforcement Learning with Logarithmic Regret and Policy Switches
HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequences
Temporally-Consistent Survival Analysis
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data
Learning and Covering Sums of Independent Random Variables with Unbounded Support
Learning to Discover and Detect Objects
UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes
BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach
DeepFoids: Adaptive Bio-Inspired Fish Simulation with Deep Reinforcement Learning
Improving Intrinsic Exploration with Language Abstractions
MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields
Monte Carlo Augmented Actor-Critic for Sparse Reward Deep Reinforcement Learning from Suboptimal Demonstrations
ShapeCrafter: A Recursive Text-Conditioned 3D Shape Generation Model
Non-identifiability and the Blessings of Misspecification in Models of Molecular Fitness
VoiceBlock: Privacy through Real-Time Adversarial Attacks with Audio-to-Audio Models
Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm
Outlier-Robust Sparse Mean Estimation for Heavy-Tailed Distributions
Invariance-Aware Randomized Smoothing Certificates
Beyond the Return: Off-policy Function Estimation under User-specified Error-measuring Distributions
On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL
Energy-Based Contrastive Learning of Visual Representations
Identifying good directions to escape the NTK regime and efficiently learn low-degree plus sparse polynomials
Deep Surrogate Assisted Generation of Environments
Hierarchical Lattice Layer for Partially Monotone Neural Networks
SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training
Self-Similarity Priors: Neural Collages as Differentiable Fractal Representations
Sample Constrained Treatment Effect Estimation
What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment
Empirical Phase Diagram for Three-layer Neural Networks with Infinite Width
FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation
Maximum Likelihood Training of Implicit Nonlinear Diffusion Model
Single Loop Gaussian Homotopy Method for Non-convex Optimization
GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations
CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning
Inducing Equilibria via Incentives: Simultaneous Design-and-Play Ensures Global Convergence
Reinforcement Learning with Neural Radiance Fields
Multi-agent Performative Prediction with Greedy Deployment and Consensus Seeking Agents
A Differentially Private Linear-Time fPTAS for the Minimum Enclosing Ball Problem
Debiased Causal Tree: Heterogeneous Treatment Effects Estimation with Unmeasured Confounding
Assistive Teaching of Motor Control Tasks to Humans
Learning interacting dynamical systems with latent Gaussian process ODEs
Provably expressive temporal graph networks
A Universal Error Measure for Input Predictions Applied to Online Graph Problems
On the difficulty of learning chaotic dynamics with RNNs
Learning on the Edge: Online Learning with Stochastic Feedback Graphs
Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks
Adjoint-aided inference of Gaussian process driven differential equations
Subquadratic Kronecker Regression with Applications to Tensor Decomposition
Post-hoc estimators for learning to defer to an expert
Polynomial-Time Optimal Equilibria with a Mediator in Extensive-Form Games
Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods
Discovered Policy Optimisation
Decomposable Non-Smooth Convex Optimization with Nearly-Linear Gradient Oracle Complexity
SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG
Convexity Certificates from Hessians
Holomorphic Equilibrium Propagation Computes Exact Gradients Through Finite Size Oscillations
Log-Linear-Time Gaussian Processes Using Binary Tree Kernels
Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples
Continual Learning In Environments With Polynomial Mixing Times
VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives
Algorithms and Hardness for Learning Linear Thresholds from Label Proportions
Enhanced Meta Reinforcement Learning via Demonstrations in Sparse Reward Environments
Make Some Noise: Reliable and Efficient Single-Step Adversarial Training
Transformer Memory as a Differentiable Search Index
(De-)Randomized Smoothing for Decision Stump Ensembles
Global Normalization for Streaming Speech Recognition in a Modular Framework
Theoretically Better and Numerically Faster Distributed Optimization with Smoothness-Aware Quantization Techniques
Learning Tractable Probabilistic Models from Inconsistent Local Estimates
List-Decodable Sparse Mean Estimation via Difference-of-Pairs Filtering
Normalizing Flows for Knockoff-free Controlled Feature Selection
Debiased Machine Learning without Sample-Splitting for Stable Estimators
Explicable Policy Search
Robustness to Unbounded Smoothness of Generalized SignSGD
Subgame Solving in Adversarial Team Games
Autoregressive Perturbations for Data Poisoning
Trust Region Policy Optimization with Optimal Transport Discrepancies: Duality and Algorithm for Continuous Actions
Statistical Learning and Inverse Problems: A Stochastic Gradient Approach
TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s
Self-Aware Personalized Federated Learning
Unsupervised Visual Representation Learning via Mutual Information Regularized Assignment
LiteTransformerSearch: Training-free Neural Architecture Search for Efficient Language Models
Nonstationary Dual Averaging and Online Fair Allocation
Leveraging Inter-Layer Dependency for Post -Training Quantization
FOF: Learning Fourier Occupancy Field for Monocular Real-time Human Reconstruction
Learning Expressive Meta-Representations with Mixture of Expert Neural Processes
REVIVE: Regional Visual Representation Matters in Knowledge-Based Visual Question Answering
Online Neural Sequence Detection with Hierarchical Dirichlet Point Process
Exploring Figure-Ground Assignment Mechanism in Perceptual Organization
DTG-SSOD: Dense Teacher Guidance for Semi-Supervised Object Detection
Deliberated Domain Bridging for Domain Adaptive Semantic Segmentation
Dual-Curriculum Contrastive Multi-Instance Learning for Cancer Prognosis Analysis with Whole Slide Images
BadPrompt: Backdoor Attacks on Continuous Prompts
Geodesic Self-Attention for 3D Point Clouds
Learning Enhanced Representation for Tabular Data via Neighborhood Propagation
Spectrum Random Masking for Generalization in Image-based Reinforcement Learning
3DB: A Framework for Debugging Computer Vision Models
High-dimensional limit theorems for SGD: Effective dynamics and critical scaling
Provable Generalization of Overparameterized Meta-learning Trained with SGD
MinVIS: A Minimal Video Instance Segmentation Framework without Video-based Training
Efficient Meta Reinforcement Learning for Preference-based Fast Adaptation
Reinforced Genetic Algorithm for Structure-based Drug Design
Motion Transformer with Global Intention Localization and Local Movement Refinement
Deep Fourier Up-Sampling
FR: Folded Rationalization with a Unified Encoder
Measures of Information Reflect Memorization Patterns
Trading off Image Quality for Robustness is not Necessary with Regularized Deterministic Autoencoders
CASA: Category-agnostic Skeletal Animal Reconstruction
Learning Energy Networks with Generalized Fenchel-Young Losses
Regularized Gradient Descent Ascent for Two-Player Zero-Sum Markov Games
Rethinking Image Restoration for Object Detection
GBA: A Tuning-free Approach to Switch between Synchronous and Asynchronous Training for Recommendation Models
Modeling Human Exploration Through Resource-Rational Reinforcement Learning
SignRFF: Sign Random Fourier Features
Gradient Estimation with Discrete Stein Operators
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems
Single-phase deep learning in cortico-cortical networks
GraphQNTK: Quantum Neural Tangent Kernel for Graph Data
BiMLP: Compact Binary Architectures for Vision Multi-Layer Perceptrons
Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent
Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Models
LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model
An $\alpha$-regret analysis of Adversarial Bilateral Trade
Intrinsic dimensionality estimation using Normalizing Flows
Supervised Training of Conditional Monge Maps
Drawing out of Distribution with Neuro-Symbolic Generative Models
Sketching based Representations for Robust Image Classification with Provable Guarantees
Learning low-dimensional generalizable natural features from retina using a U-net
Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome
VisCo Grids: Surface Reconstruction with Viscosity and Coarea Grids
Synergy-of-Experts: Collaborate to Improve Adversarial Robustness
Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondence
Fast Bayesian Estimation of Point Process Intensity as Function of Covariates
MOVE: Unsupervised Movable Object Segmentation and Detection
Not All Bits have Equal Value: Heterogeneous Precisions via Trainable Noise
Differentially Private Learning Needs Hidden State (Or Much Faster Convergence)
Training Spiking Neural Networks with Local Tandem Learning
Unsupervised Skill Discovery via Recurrent Skill Training
Interpreting Operation Selection in Differentiable Architecture Search: A Perspective from Influence-Directed Explanations
Fair Rank Aggregation
Optimal Gradient Sliding and its Application to Optimal Distributed Optimization Under Similarity
Contact-aware Human Motion Forecasting
Non-rigid Point Cloud Registration with Neural Deformation Pyramid
Make an Omelette with Breaking Eggs: Zero-Shot Learning for Novel Attribute Synthesis
The First Optimal Algorithm for Smooth and Strongly-Convex-Strongly-Concave Minimax Optimization
Towards Reasonable Budget Allocation in Untargeted Graph Structure Attacks via Gradient Debias
Stability and Generalization of Kernel Clustering: from Single Kernel to Multiple Kernel
Few-shot Relational Reasoning via Connection Subgraph Pretraining
Alleviating Adversarial Attacks on Variational Autoencoders with MCMC
Coreset for Line-Sets Clustering
Fast Stochastic Composite Minimization and an Accelerated Frank-Wolfe Algorithm under Parallelization
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
HF-NeuS: Improved Surface Reconstruction Using High-Frequency Details
On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning
Spatial Pruned Sparse Convolution for Efficient 3D Object Detection
Byzantine Spectral Ranking
What Can the Neural Tangent Kernel Tell Us About Adversarial Robustness?
On Translation and Reconstruction Guarantees of the Cycle-Consistent Generative Adversarial Networks
Evaluated CMI Bounds for Meta Learning: Tightness and Expressiveness
SnAKe: Bayesian Optimization with Pathwise Exploration
Random Rank: The One and Only Strategyproof and Proportionally Fair Randomized Facility Location Mechanism
Resolving the data ambiguity for periodic crystals
CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion
Coresets for Vertical Federated Learning: Regularized Linear Regression and $K$-Means Clustering
Learning Predictions for Algorithms with Predictions
Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble Solution
DASCO: Dual-Generator Adversarial Support Constrained Offline Reinforcement Learning
Exploring through Random Curiosity with General Value Functions
Equivariant Networks for Zero-Shot Coordination
A PAC-Bayesian Generalization Bound for Equivariant Networks
Split-kl and PAC-Bayes-split-kl Inequalities for Ternary Random Variables
Pareto Set Learning for Expensive Multi-Objective Optimization
Formalizing Consistency and Coherence of Representation Learning
Compositional generalization through abstract representations in human and artificial neural networks
The Sample Complexity of One-Hidden-Layer Neural Networks
Diffusion Visual Counterfactual Explanations
Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budget
Pessimism for Offline Linear Contextual Bandits using $\ell_p$ Confidence Sets
Assaying Out-Of-Distribution Generalization in Transfer Learning
What are the best Systems? New Perspectives on NLP Benchmarking
Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise
Hardness in Markov Decision Processes: Theory and Practice
Generalization Error Bounds on Deep Learning with Markov Datasets
Information-Theoretic Safe Exploration with Gaussian Processes
M³ViT: Mixture-of-Experts Vision Transformer for Efficient Multi-task Learning with Model-Accelerator Co-design
HierSpeech: Bridging the Gap between Text and Speech by Hierarchical Variational Inference using Self-supervised Representations for Speech Synthesis
[Re] Replication Study of "Fairness and Bias in Online Selection"
Triangulation candidates for Bayesian optimization
Non-asymptotic and Accurate Learning of Nonlinear Dynamical Systems
Washing The Unwashable : On The (Im)possibility of Fairwashing Detection
No-regret learning in games with noisy feedback: Faster rates and adaptivity via learning rate separation
Adaptive Data Debiasing through Bounded Exploration
Positive-Unlabeled Learning using Random Forests via Recursive Greedy Risk Minimization
Toward a realistic model of speech processing in the brain with self-supervised learning
TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets
CLIPDraw: Exploring Text-to-Drawing Synthesis through Language-Image Encoders
Attention-based Neural Cellular Automata
Sparse Additive Gaussian Process Regression
Attraction-Repulsion Spectrum in Neighbor Embeddings
Online Nonnegative CP-dictionary Learning for Markovian Data
Decimated Framelet System on Graphs and Fast G-Framelet Transforms
Multi-Agent Multi-Armed Bandits with Limited Communication
Accelerated Zeroth-Order and First-Order Momentum Methods from Mini to Minimax Optimization
Optimality and Stability in Non-Convex Smooth Games
Deep Limits and a Cut-Off Phenomenon for Neural Networks
Robust and scalable manifold learning via landmark diffusion for long-term medical signal processing
[Re] Differentiable Spatial Planning using Transformers
All You Need is a Good Functional Prior for Bayesian Deep Learning
Recovery and Generalization in Over-Realized Dictionary Learning
Truncated Emphatic Temporal Difference Methods for Prediction and Control
[Re] AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients
When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint
[Re] Replication study of 'Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling'
Learning Operators with Coupled Attention
[Re] Solving Phase Retrieval With a Learned Reference
[Re] Explaining in Style: Training a GAN to explain a classifier in StyleSpace
DeepInteraction: 3D Object Detection via Modality Interaction
Mix and Reason: Reasoning over Semantic Topology with Data Mixing for Domain Generalization
SGAM: Building a Virtual 3D World through Simultaneous Generation and Mapping
RLIP: Relational Language-Image Pre-training for Human-Object Interaction Detection
Dense Interspecies Face Embedding
Orthogonal Transformer: An Efficient Vision Transformer Backbone with Token Orthogonalization
UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup
Rethinking Individual Global Max in Cooperative Multi-Agent Reinforcement Learning
Resource-Adaptive Federated Learning with All-In-One Neural Composition
One Model to Edit Them All: Free-Form Text-Driven Image Manipulation with Semantic Modulations
Weakly-Supervised Multi-Granularity Map Learning for Vision-and-Language Navigation
On the Robustness of Deep Clustering Models: Adversarial Attacks and Defenses
Uncoupled Learning Dynamics with $O(\log T)$ Swap Regret in Multiplayer Games
Weak-shot Semantic Segmentation via Dual Similarity Transfer
Effective Backdoor Defense by Exploiting Sensitivity of Poisoned Samples
Obj2Seq: Formatting Objects as Sequences with Class Prompt for Visual Tasks
OTKGE: Multi-modal Knowledge Graph Embeddings via Optimal Transport
Positively Weighted Kernel Quadrature via Subsampling
LASSIE: Learning Articulated Shapes from Sparse Image Ensemble via 3D Part Discovery
A Kernelised Stein Statistic for Assessing Implicit Generative Models
E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance
EpiGRAF: Rethinking training of 3D GANs
Bridging the Gap Between Vision Transformers and Convolutional Neural Networks on Small Datasets
Optimal Efficiency-Envy Trade-Off via Optimal Transport
Generating Long Videos of Dynamic Scenes
Private Synthetic Data for Multitask Learning and Marginal Queries
Graph Self-supervised Learning with Accurate Discrepancy Learning
Independence Testing for Bounded Degree Bayesian Networks
Tikhonov Regularization is Optimal Transport Robust under Martingale Constraints
ZeroC: A Neuro-Symbolic Model for Zero-shot Concept Recognition and Acquisition at Inference Time
SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image collections
Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Models
Bayesian Persuasion for Algorithmic Recourse
Deep Hierarchical Planning from Pixels
Noise Attention Learning: Enhancing Noise Robustness by Gradient Scaling
Neural Basis Models for Interpretability
Hierarchical classification at multiple operating points
Information-Theoretic GAN Compression with Variational Energy-based Model
Redistribution of Weights and Activations for AdderNet Quantization
Deep invariant networks with differentiable augmentation layers
Convergence beyond the over-parameterized regime using Rayleigh quotients
Robust $\phi$-Divergence MDPs
ToDD: Topological Compound Fingerprinting in Computer-Aided Drug Discovery
On Privacy and Personalization in Cross-Silo Federated Learning
Differentially Private Covariance Revisited
Learning Graph-embedded Key-event Back-tracing for Object Tracking in Event Clouds
Distributional Convergence of the Sliced Wasserstein Process
Homomorphic Matrix Completion
Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation
On the Identifiability of Nonlinear ICA: Sparsity and Beyond
Museformer: Transformer with Fine- and Coarse-Grained Attention for Music Generation
Towards Diverse and Faithful One-shot Adaption of Generative Adversarial Networks
Dance of SNN and ANN: Solving binding problem by combining spike timing and reconstructive attention
Efficient Sampling on Riemannian Manifolds via Langevin MCMC
ATD: Augmenting CP Tensor Decomposition by Self Supervision
Imitating Past Successes can be Very Suboptimal
RKHS-SHAP: Shapley Values for Kernel Methods
SAPD+: An Accelerated Stochastic Method for Nonconvex-Concave Minimax Problems
On Scalable Testing of Samplers
Markovian Interference in Experiments
DP-PCA: Statistically Optimal and Differentially Private PCA
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning
Continual learning: a feature extraction formalization, an efficient algorithm, and fundamental obstructions
Functional Ensemble Distillation
Self-explaining deep models with logic rule reasoning
Benign Underfitting of Stochastic Gradient Descent
Modeling the Machine Learning Multiverse
Stability Analysis and Generalization Bounds of Adversarial Training
Exact Shape Correspondence via 2D graph convolution
A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning
How and Why to Manipulate Your Own Agent: On the Incentives of Users of Learning Agents
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models
Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks
Stochastic Online Learning with Feedback Graphs: Finite-Time and Asymptotic Optimality
Spectral Bias Outside the Training Set for Deep Networks in the Kernel Regime
First Hitting Diffusion Models for Generating Manifold, Graph and Categorical Data
Universal Rates for Interactive Learning
DGD^2: A Linearly Convergent Distributed Algorithm For High-dimensional Statistical Recovery
Single-Stage Visual Relationship Learning using Conditional Queries
Pruning has a disparate impact on model accuracy
Teacher Forcing Recovers Reward Functions for Text Generation
Model-based RL with Optimistic Posterior Sampling: Structural Conditions and Sample Complexity
Optimal Dynamic Regret in LQR Control
Generalization Gap in Amortized Inference
Near-Optimal Private and Scalable $k$-Clustering
Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners
Hedging as Reward Augmentation in Probabilistic Graphical Models
Training Subset Selection for Weak Supervision
Online Reinforcement Learning for Mixed Policy Scopes
Branch & Learn for Recursively and Iteratively Solvable Problems in Predict+Optimize
Your Out-of-Distribution Detection Method is Not Robust!
An efficient graph generative model for navigating ultra-large combinatorial synthesis libraries
Communication-Efficient Topologies for Decentralized Learning with $O(1)$ Consensus Rate
Rethinking the Reverse-engineering of Trojan Triggers
Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets
On the Epistemic Limits of Personalized Prediction
Learning to Mitigate AI Collusion on Economic Platforms
STNDT: Modeling Neural Population Activity with Spatiotemporal Transformers
Masked Autoencoding for Scalable and Generalizable Decision Making
Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs
DiSC: Differential Spectral Clustering of Features
Personalized Online Federated Learning with Multiple Kernels
Patching open-vocabulary models by interpolating weights
Concrete Score Matching: Generalized Score Matching for Discrete Data
LBD: Decouple Relevance and Observation for Individual-Level Unbiased Learning to Rank
Palm up: Playing in the Latent Manifold for Unsupervised Pretraining
Focal Modulation Networks
S2P: State-conditioned Image Synthesis for Data Augmentation in Offline Reinforcement Learning
Exploitability Minimization in Games and Beyond
FeLMi : Few shot Learning with hard Mixup
The First Optimal Acceleration of High-Order Methods in Smooth Convex Optimization
On Optimal Learning Under Targeted Data Poisoning
The computational and learning benefits of Daleian neural networks
Support Recovery in Sparse PCA with Incomplete Data
Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo
Private Isotonic Regression
Do Residual Neural Networks discretize Neural Ordinary Differential Equations?
Continuous MDP Homomorphisms and Homomorphic Policy Gradient
Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks
Look where you look! Saliency-guided Q-networks for generalization in visual Reinforcement Learning
Constrained GPI for Zero-Shot Transfer in Reinforcement Learning
Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution
A Boosting Approach to Reinforcement Learning
DataMUX: Data Multiplexing for Neural Networks
Are Defenses for Graph Neural Networks Robust?
Adversarial Robustness is at Odds with Lazy Training
Robust Reinforcement Learning using Offline Data
Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits
Fine-tuning language models to find agreement among humans with diverse preferences
Tsetlin Machine for Solving Contextual Bandit Problems
Multi-Class $H$-Consistency Bounds
Statistical, Robustness, and Computational Guarantees for Sliced Wasserstein Distances
Lifting Weak Supervision To Structured Prediction
Learning Concept Credible Models for Mitigating Shortcuts
LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning
Disentangling Transfer in Continual Reinforcement Learning
Distinguishing discrete and continuous behavioral variability using warped autoregressive HMMs
Off-Team Learning
LAMP: Extracting Text from Gradients with Language Model Priors
4D Unsupervised Object Discovery
Bayesian subset selection and variable importance for interpretable prediction and classification
Unifying Voxel-based Representation with Transformer for 3D Object Detection
Multi-Scale Adaptive Network for Single Image Denoising
Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors
Efficient Graph Similarity Computation with Alignment Regularization
Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering
Falconn++: A Locality-sensitive Filtering Approach for Approximate Nearest Neighbor Search
Natural image synthesis for the retina with variational information bottleneck representation
A Lower Bound of Hash Codes' Performance
I2Q: A Fully Decentralized Q-Learning Algorithm
Shadow Knowledge Distillation: Bridging Offline and Online Knowledge Transfer
A Differentiable Semantic Metric Approximation in Probabilistic Embedding for Cross-Modal Retrieval
Heterogeneous Skill Learning for Multi-agent Tasks
Are You Stealing My Model? Sample Correlation for Fingerprinting Deep Neural Networks
Model-Based Opponent Modeling
When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture
Learning Invariant Graph Representations for Out-of-Distribution Generalization
Learning from Future: A Novel Self-Training Framework for Semantic Segmentation
Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstruction
Wasserstein Iterative Networks for Barycenter Estimation
Personalized Federated Learning towards Communication Efficiency, Robustness and Fairness
Analyzing Sharpness along GD Trajectory: Progressive Sharpening and Edge of Stability
Rashomon Capacity: A Metric for Predictive Multiplicity in Classification
Pre-trained Adversarial Perturbations
Conformal Frequency Estimation with Sketched Data
Convergent Representations of Computer Programs in Human and Artificial Neural Networks
tntorch: Tensor Network Learning with PyTorch
Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization
Joint Entropy Search for Multi-Objective Bayesian Optimization
Embracing Consistency: A One-Stage Approach for Spatio-Temporal Video Grounding
A Closer Look at the Adversarial Robustness of Deep Equilibrium Models
Language Conditioned Spatial Relation Reasoning for 3D Object Grounding
[Re] Projection-based Algorithm for Updating the TruncatedSVD of Evolving Matrices
Unsupervised Point Cloud Completion and Segmentation by Generative Adversarial Autoencoding Network
Audio-Driven Co-Speech Gesture Video Generation
Dynamic Graph Neural Networks Under Spatio-Temporal Distribution Shift
On the role of overparameterization in off-policy Temporal Difference learning with linear function approximation
Quantized Training of Gradient Boosting Decision Trees
InterpretDL: Explaining Deep Models in PaddlePaddle
Efficient Change-Point Detection for Tackling Piecewise-Stationary Bandits
A Unified Statistical Learning Model for Rankings and Scores with Application to Grant Panel Review
LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data
D-GCCA: Decomposition-based Generalized Canonical Correlation Analysis for Multi-view High-dimensional Data
Supervised Dimensionality Reduction and Visualization using Centroid-Encoder
Foolish Crowds Support Benign Overfitting
Rethinking Nonlinear Instrumental Variable Models through Prediction Validity
[Re] Reproduction and Extension of "Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation"
[Re] An Implementation of Fair Robust Learning
GAMA: Generative Adversarial Multi-Object Scene Attacks
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training
An Empirical Study on Disentanglement of Negative-free Contrastive Learning
Social-Inverse: Inverse Decision-making of Social Contagion Management with Task Migrations
Bayesian Risk Markov Decision Processes
SHINE: SubHypergraph Inductive Neural nEtwork
Singular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuning
Non-deep Networks
Feature-Proxy Transformer for Few-Shot Segmentation
Estimating and Explaining Model Performance When Both Covariates and Labels Shift
The alignment property of SGD noise and how it helps select flat minima: A stability analysis
Thompson Sampling Efficiently Learns to Control Diffusion Processes
Fair and Efficient Allocations Without Obvious Manipulations
Fuzzy Learning Machine
ASPiRe: Adaptive Skill Priors for Reinforcement Learning
Sound and Complete Causal Identification with Latent Variables Given Local Background Knowledge
Explainability Via Causal Self-Talk
ALIFE: Adaptive Logit Regularizer and Feature Replay for Incremental Semantic Segmentation
Causal Identification under Markov equivalence: Calculus, Algorithm, and Completeness
Random Normalization Aggregation for Adversarial Defense
Size and depth of monotone neural networks: interpolation and approximation
Spartan: Differentiable Sparsity via Regularized Transportation
On Gap-dependent Bounds for Offline Reinforcement Learning
Revisiting Sliced Wasserstein on Images: From Vectorization to Convolution
Accelerated Projected Gradient Algorithms for Sparsity Constrained Optimization Problems
Context-Based Dynamic Pricing with Partially Linear Demand Model
S-PIFu: Integrating Parametric Human Models with PIFu for Single-view Clothed Human Reconstruction
Action-modulated midbrain dopamine activity arises from distributed control policies
NUWA-Infinity: Autoregressive over Autoregressive Generation for Infinite Visual Synthesis
Bezier Gaussian Processes for Tall and Wide Data
Minimax Regret for Cascading Bandits
Pre-activation Distributions Expose Backdoor Neurons
Posterior Matching for Arbitrary Conditioning
Alternating Mirror Descent for Constrained Min-Max Games
Early Stage Convergence and Global Convergence of Training Mildly Parameterized Neural Networks
Foundation Posteriors for Approximate Probabilistic Inference
Entropy-Driven Mixed-Precision Quantization for Deep Network Design
Denoising Diffusion Restoration Models
Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms
A Combinatorial Perspective on the Optimization of Shallow ReLU Networks
A Lagrangian Duality Approach to Active Learning
A Statistical Online Inference Approach in Averaged Stochastic Approximation
Theoretical analysis of deep neural networks for temporally dependent observations
On the Complexity of Adversarial Decision Making
Scalable Distributional Robustness in a Class of Non-Convex Optimization with Guarantees
Uncovering the Structural Fairness in Graph Contrastive Learning
ULNeF: Untangled Layered Neural Fields for Mix-and-Match Virtual Try-On
Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces
Better Uncertainty Calibration via Proper Scores for Classification and Beyond
Augmenting Online Algorithms with $\varepsilon$-Accurate Predictions
Asymptotics of $\ell_2$ Regularized Network Embeddings
Deep Counterfactual Estimation with Categorical Background Variables
What is a Good Metric to Study Generalization of Minimax Learners?
Non-Convex Bilevel Games with Critical Point Selection Maps
The Franz-Parisi Criterion and Computational Trade-offs in High Dimensional Statistics
Provable Defense against Backdoor Policies in Reinforcement Learning
IMED-RL: Regret optimal learning of ergodic Markov decision processes
Leveraging the Hints: Adaptive Bidding in Repeated First-Price Auctions
A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases
Distributed Learning of Finite Gaussian Mixtures
[Re] Reproducibility Report: Contrastive Learning of Socially-aware Motion Representations
[Re] GANSpace: Discovering Interpretable GAN Controls
[Re] Reproducibility Study of “Counterfactual Generative Networks”
[Re] Does Self-Supervision Always Improve Few-Shot Learning?
On Kernelized Multi-Armed Bandits with Constraints
A Nonconvex Framework for Structured Dynamic Covariance Recovery
A Mean-Field Game Approach to Cloud Resource Management with Function Approximation
CoupAlign: Coupling Word-Pixel with Sentence-Mask Alignments for Referring Image Segmentation
Fast and Robust Rank Aggregation against Model Misspecification
Poisson Flow Generative Models
Boosting Out-of-distribution Detection with Typical Features
Joint Estimation and Inference for Data Integration Problems based on Multiple Multi-layered Gaussian Graphical Models
Conditional Diffusion Process for Inverse Halftoning
Knowledge Distillation from A Stronger Teacher
Dynamic pricing and assortment under a contextual MNL demand
Temporal Effective Batch Normalization in Spiking Neural Networks
The trade-offs of model size in large recommendation models : 100GB to 10MB Criteo-tb DLRM model
Neural Transmitted Radiance Fields
🏘️ ProcTHOR: Large-Scale Embodied AI Using Procedural Generation
[Re] Exacerbating Algorithmic Bias through Fairness Attacks
Transfer Learning in Information Criteria-based Feature Selection
Learning with little mixing
Fairness-Aware PAC Learning from Corrupted Data
Online Frank-Wolfe with Arbitrary Delays
Online Allocation and Learning in the Presence of Strategic Agents
Weisfeiler and Leman Go Walking: Random Walk Kernels Revisited
Sufficient reductions in regression with mixed predictors
Optimal Query Complexities for Dynamic Trace Estimation
Understanding Benign Overfitting in Gradient-Based Meta Learning
Generalised Mutual Information for Discriminative Clustering
Prune and distill: similar reformatting of image information along rat visual cortex and deep neural networks
ShuffleMixer: An Efficient ConvNet for Image Super-Resolution
Minimax Optimal Fixed-Budget Best Arm Identification in Linear Bandits
[Re] Strategic classification made practical: reproduction
Communication-efficient distributed eigenspace estimation with arbitrary node failures
Generalised Implicit Neural Representations
AttCAT: Explaining Transformers via Attentive Class Activation Tokens
Score-Based Generative Models Detect Manifolds
Geodesic Graph Neural Network for Efficient Graph Representation Learning
[Re] Nondeterminism and Instability in Neural Network Optimization
Diffusion-based Molecule Generation with Informative Prior Bridges
[Re] Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction
The Importance of Being Correlated: Implications of Dependence in Joint Spectral Inference across Multiple Networks
Zero-shot Transfer Learning within a Heterogeneous Graph via Knowledge Transfer Networks
Provably sample-efficient RL with side information about latent dynamics
Signal Processing for Implicit Neural Representations
Embrace the Gap: VAEs Perform Independent Mechanism Analysis
Exponential Separations in Symmetric Neural Networks
[Re] Understanding Self-Supervised Learning Dynamics without Contrastive Pairs
Transformers from an Optimization Perspective
(f,Gamma)-Divergences: Interpolating between f-Divergences and Integral Probability Metrics
PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies
[Re] Transparent Object Tracking Benchmark
Neural Lyapunov Control of Unknown Nonlinear Systems with Stability Guarantees
[Re] Graph Edit Networks
[Re] Reproduction Study of Variational Fair Clustering
BR-SNIS: Bias Reduced Self-Normalized Importance Sampling
Fair and Optimal Decision Trees: A Dynamic Programming Approach
Brain Network Transformer
Learning to Navigate Wikipedia by Taking Random Walks
The Neural Testbed: Evaluating Joint Predictions
A Bregman Learning Framework for Sparse Neural Networks
[Re] Learning to count everything
On the Double Descent of Random Features Models Trained with SGD

Download Research Papers and Scientific Articles for free (Sci-Hub and Library Genesis links updated August 2022)

Many students and researchers need to find a paper for their research, to complete the review of an article, or while writing their thesis. Many papers can be found through your university library, but for those that you may not have access to through your institution, we take a look at the three largest open access sites, as well as sci hub and Library Genesis .

Unpaywall Unpaywall is a website built by Impactstory, a nonprofit working to make science more open and reusable online. They are supported by grants from the National Science Foundation and the Alfred P. Sloan Foundation. What they do is gather all the articles they can from all the open-access repositories on the internet. These are papers that have been provided by the authors or publishers for free, and thus Unpaywall is completely legal. They say they have about 50-85% of all scientific articles available in their archive. Works with Chrome or Firefox.

PaperPanda PaperPanda is a free browser extension for Chrome that gives you one-click access to papers and journal articles. When you find a paper on the publisher’s site, just click the PaperPanda icon and the panda goes and finds the PDF for you.

Open Access Button The Open Access Button does something very similar to Unpaywall, with some major differences. They search thousands of public repositories, and if the article is not in any of them they send a request to the author to make the paper publicly available with them. The more people try to find an article through them, the more requests an author gets. You can search for articles/papers directly from their page, or download their browser extension.

Library Genesis Library Genesis is a database of over 5 million (yes, million) free papers, articles, entire journals, and non-fiction books. They also have comics, fiction books, and books in many non-english languages. They are also known as LibGen or Genesis Library. Many of the papers on Library Genesis are the same as sci hub, but what sets them apart is that Library Genesis has books as well.

OAmg OAmg lets you search for journal articles and papers, download them, and of course cite them in your Citationsy projects. After entering a query it searches through all published papers in the world and shows you the matches. You can then click a result to see more details and read a summary. It will also let you download the paper through a couple different, completely legal open access services. www.oa.mg

Sci-Hub (link updated August 2022) Finally, there’s Sci Hub . Science-Hub works in a completely different way than the other two: researchers, students, and other academics donate their institutional login to Schi-Hub, and when you search for a paper they download it through that account. After the articles has been downloaded they store a copy of it on their own servers. You can basically download 99% of all scientific articles and papers on SciHub. Just enter the DOI to download the papers you need for free from scihub. Shihub was launched by the researcher Alexandra Elbakyan in 2011 with the goal of providing free access to research to everyone, not only those who have the money to pay for journals. Many in the scientific community praise hub-sci / sciencehub for furthering the knowledge of humankind and helping academics from all over the world. shi hub has been sued many times by publishers like Elsevier but it is still accessible, for example by using a sci hub proxy.

You can find links to Sci-Hub on Wikipedia ( https://en.wikipedia.org/wiki/Sci-Hub ) or WikiData ( https://www.wikidata.org/wiki/Q21980377#P856 ).

Referencing and Writing Advice Unlocking Knowledge Getting the green light when using plagiarism detection software doesn’t mean you haven’t plagiarised.

access millions of research papers with Paper Panda

So easy to use, such a huge life saver in terms of research and assessments !! have not come across a article i can not, not read thanks to this :D so happy and has made life easier, thank you, thank you, thank you

One of the most useful web extensions I have ever used, Thank you to the girl on Tiktok that recommended it. I hope both sides of her pillow are cold everyday. Thank you to the developers as well. Huge thumbs up.

es muy bueno!!!! y muy útil!!! estoy literalmente llorando de la felicidad porque ahora puedo ver ensayos o artículos que antes necesitaba y no podía, y además es muy fácil de usar muy recomendado

It is soooooo amazing ,easy to use and it works like a charm. Helped me a great deal!! Thank you so much

Works great. Been using it only for a short while, but it didn’t fail yet. It’s also very quick. Downside is that it sometimes downloaded an early version of the paper, not the final published paper.

very helpful in searching scientific articles

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An amazing extension for a researcher.

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I wish I found this earlier. It works perfectly, easy to access any articles

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A melhor extensão!

“Paywall? What’s that?”

Paperpanda searches the web for pdf s so you don’t have to, i’m here to help, you’ve probably run into this problem – you want to read a paper, but it’s locked behind a paywall. maybe you have access to it through your library or university, maybe it’s available to download for free through an open access portal, maybe the author uploaded a pdf to a website somewhere – but how are you going to find it paperpanda is here to help just click the tiny panda in your toolbar and the panda will run off and find the paper for you., access research papers in one click, save time accessing full-text pdf s with the free paperpanda browser plugin, stop clicking and start reading, stop navigating paywalls, search engines, and logins. paperpanda helps you get that full-text pdf faster.

Research guidance, Research Journals, Top Universities

How to use SCI HUB to download research papers for free

Use Sci-Hub to download research papers

The Sci-Hub project supports the Open Access movement in science. It provides mass & public access to research papers.

Often we have reported that most of the research papers published by some reputed journals are paid. If anyone wants to download such manuscripts, he needs to pay to access such papers.

SCI Hub allows downloading and reading such papers for free . Sci-H ub contains most of the academic and scientific papers. What one has to do is visit the site after finding the research paper link or DOI of the journal article . You can paste the DOI or URL in the search button and click search. If the paper is available, a preview will be shown. You can download this paper and use it for your reference.

Researchers most often use SCI HUB to download research papers for free.

How to use Sci Hub?

Follow the below steps to download paid researchers papers for free using Sci-Hub.

Step 1: Go to the official website of SCI- HUB .

Step 2: Enter the Title/ DOI/ URL of the research paper which you want to download/ read using SCI HUB.

Step 3: Click on Open or press enter key.

Step 4: As soon as you perform step 3, the desired research paper will visible on the website. You can download the paper from click on the download icon.

Thanks for reading our article. I hope you like it.

After reading this article, now you can download IEEE/ Springer/ Elsevier paper.

Download Springer journals articles for free

Download Elsevier journals articles for free

14 Websites to Download Research Paper for Free – 2024

14 Top Websites for Free, Open Access Research Paper Download

Collecting and reading relevant research articles to one’s research areas is important for PhD scholars. However, for any research scholar, downloading a research paper is one of the most difficult tasks. You must pay for access to high-quality research materials or subscribe to the journal or publication. In this article, ilovephd lists the top 14 websites to download free research papers, journals, books, datasets, patents, and conference proceedings downloads.

Free Research Paper Download Websites – 2024

Check the 14 best free websites to download and read research papers listed below:

Sci-Hub is a website link with over 64.5 million academic papers and articles available for direct download. It bypasses publisher paywalls by allowing access through educational institution proxies. To download papers Sci-Hub stores papers in its repository, this storage is called Library Genesis (LibGen) or Library Genesis Proxy 2024. It helps researchers to download free articles by simply using Digital Object Identifier (DOI) of the article.

Visit: Working Sci-Hub Proxy Links – 2024

2. Z-Library

The Z-Library clones Library Genesis, a shadow library project. Z-Library facilitates file sharing of scholarly journal articles, academic texts, and general-interest books (including some copyrighted materials). While most of its books come from Library Genesis, further expanding the collection, users can also directly upload content to the site. This user-contributed content helps to make literature even more widely available. Additionally, individuals can donate to the website’s repository, furthering their mission of free access.

Z-Library claims to have a massive collection, boasting more than 10,139,382 Books books and 84,837,646 Articles articles as of April 25, 2024. According to the project’s page for academic publications (at booksc.org), it aspires to be “the world’s largest e-book library” as well as “the world’s largest scientific papers repository.” Interestingly, Z-Library also describes itself as a donation-based non-profit organization.

Visit Z-Library – You can Download 70,000,000+ scientific articles for free

3. Library Genesis

The Library Genesis aggregator is a community aiming to collect and catalog item descriptions for the most scientific, scientific, and technical directions, as well as file metadata. In addition to the descriptions, the aggregator contains only links to third-party resources hosted by users. All information posted on the website is collected from publicly available public Internet resources and is intended solely for informational purposes.

Visit: libgen.li

4. Unpaywall – Free Research Paper Download

Unpaywall harvests Open Access content from over 50,000 publishers and repositories, and makes it easy to find, track, and use. It is integrated into thousands of library systems, search platforms, and other information products worldwide. If you’re involved in scholarly communication, there’s a good chance you’ve already used Unpaywall data.

Unpaywall is run by OurResearch, a nonprofit dedicated to making scholarships more accessible to everyone. Open is our passion. So it’s only natural our source code is open, too.

Visit: unpaywall.org

5. GetTheResearch.org

GetTheResearch.org is an Artificial Intelligence(AI) powered search engine for searching and understanding scientific articles for researchers and scientists. It was developed as a part of the Unpaywall project. Unpaywall is a database of 23,329,737 free scholarly Open Access(OA) articles from over 50,000 publishers and repositories, and make it easy to find, track, and use.

Visit: Find and Understand 25 Million Peer-Reviewed Research Papers for Free

6. Directory of Open Access Journals (DOAJ)

DOAJ (Directory of Open Access Journals) was launched in 2003 with 300 open-access journals. Today, this independent index contains almost 17,500 peer-reviewed, open-access journals covering all areas of science, technology, medicine, social sciences, arts, and humanities. Open-access journals from all countries and in all languages are accepted for indexing.

DOAJ is financially supported by many libraries, publishers, and other like-minded organizations. Supporting DOAJ demonstrates a firm commitment to open access and the infrastructure that supports it.

Visit: doaj.org

7. Researcher

The Researcher is a free journal-finding mobile application that helps you to read new journal papers every day that are relevant to your research. It is the most popular mobile application used by more than 3 million scientists and researchers to keep themselves updated with the latest academic literature.

Visit: 10 Best Apps for Graduate Students

8. Science Open

ScienceOpen is a discovery platform with interactive features for scholars to enhance their research in the open, make an impact, and receive credit for it. It provides context-building services for publishers, to bring researchers closer to the content than ever before. These advanced search and discovery functions, combined with post-publication peer review, recommendation, social sharing, and collection-building features make ScienceOpen the only research platform you’ll ever need.

Visit: scienceopen.com

OA.mg is a search engine for academic papers. Whether you are looking for a specific paper, or for research from a field, or all of an author’s works – OA.mg is the place to find it.

Visit: oa.mg

10. Internet Archive Scholar

Internet Archive Scholar (IAS) is a full-text search index that includes over 25 million research articles and other scholarly documents preserved in the Internet Archive. The collection spans from digitized copies of eighteenth-century journals through the latest Open Access conference proceedings and pre-prints crawled from the World Wide Web.

Visit: Sci hub Alternative – Internet Archive Scholar

11. Citationsy Archives

Citationsy was founded in 2017 after the reference manager Cenk was using at the time, RefMe, was shut down. It was immediately obvious that the reason people loved RefMe — a clean interface, speed, no ads, and simplicity of use — did not apply to CiteThisForMe. It turned out to be easier than anticipated to get a rough prototype up.

Visit: citationsy.com

CORE is the world’s largest aggregator of open-access research papers from repositories and journals. It is a not-for-profit service dedicated to the open-access mission. We serve the global network of repositories and journals by increasing the discoverability and reuse of open-access content.

It provides solutions for content management, discovery, and scalable machine access to research. Our services support a wide range of stakeholders, specifically researchers, the general public, academic institutions, developers, funders, and companies from a diverse range of sectors including but not limited to innovators, AI technology companies, digital library solutions, and pharma.

Visit: core.ac.uk

13. Dimensions

The database called “Dimensions” covers millions of research publications connected by more than 1.6 billion citations, supporting grants, datasets, clinical trials, patents, and policy documents.

Dimensions is the most comprehensive research grants database that links grants to millions of resulting publications, clinical trials, and patents. It

provides up-to-the-minute online attention data via Altmetric, showing you how often publications and clinical trials are discussed around the world. 226m Altmetric mentions with 17m links to publications.

Dimensions include datasets from repositories such as Figshare, Dryad, Zenodo, Pangaea, and many more. It hosts millions of patents with links to other citing patents as well as to publications and supporting grants.

Visit: dimensions.ai

14. PaperPanda – Download Research Papers for Free

PaperPanda is a Chrome extension that uses some clever logic and the Panda’s detective skills to find you the research paper PDFs you need. Essentially, when you activate PaperPanda it finds the DOI of the paper from the current page, and then goes and searches for it. It starts by querying various Open Access repositories like OpenAccessButton, OaDoi, SemanticScholar, Core, ArXiV, and the Internet Archive. You can also set your university library’s domain in the settings (this feature is in the works and coming soon). PaperPanda will then automatically search for the paper through your library. You can also set a different custom domain in the settings.

Visit: PaperPanda

I hope this article will help you to know some of the best websites to download research papers and journals for free. By utilizing open-access databases, free search tools, and potentially even your local university library, you can access a wealth of valuable scholarly information without infringing on a copyright. Remember, ethical practices in research paper downloading are important, so always prioritize legal access to materials whenever possible. Happy researching!

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Example of Abstract for Research Paper – Tips and Dos and Donts

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Top 11 Websites for Free Research Paper Downloads

For PhD researchers, it’s critical to gather and read research publications that are pertinent to their areas of study. However, downloading a research paper is one of the most challenging chores for any research scholar. To gain access to high-quality research resources, one needs to pay a fee or subscribe to a journal or publication. In this post, We have shown you how to get a research paper for free.

Sci-Hub was originally launched by Alexandra Elbakyan, a Kazakhstani graduate student, in 2011. It is a website known for providing access to various academic articles and papers using educational institution access and its own collection of downloaded articles and papers. In fact, you can download almost 99% of all scientific papers and articles in existence on Sci-Hub.

Many internet service providers (especially in developed countries) have blocked it at present. Sci-Hub’s own statistics show that the chances of a request for download being successful are 99%. It processes more than 200,000 requests every day.

How to use Sci-Hub?

Visit https://sci-hub.se/ (Use a VPN to access it if blocked.) You can also checkout Visit: Working Sci-Hub Proxy Links – 2022 ( https://www.ilovephd.com/working-sci-hub-proxy-links-updated/ )
Enter the full name of the DOI, URL, or URL in the paper that you would like to download.
Select”Open” or click the “Open” click.

2. Library Genesis

Library Genesis (Libgen) is a file-sharing based shadow library website for scholarly journal articles, academic and general-interest books, images, comics, audiobooks, and magazines. The site enables free access to content that is otherwise paywalled or not digitized elsewhere. This website was threatened with legal action by Elsevier one of the largest publishing companies of technical, scientific medical and scientific research papers in the year 2015.

You can find a research paper or book on Library Genesis by following the steps given below:

Visit Library Genesis’ official website (libgen.li).
Type the name of whatever you’re looking for into the search field, and click the “search!” button.
Click on the name of a book or research paper in the list of results, and choose one of the available mirrors.
Proceed to download the book or research paper and save it to your device.

3. Z-Library

Z-Library is a clone of Library Genesis, a shadow library project that allows users to share scholarly journal articles, academic texts, and general-interest books via file sharing (some of which are pirated). The majority of its books come from Library Genesis, however, some are posted directly to the site by individuals.

Individuals can also donate to the website’s repository to make literature more widely available. Z-library claims to have more than 10,139,382 Books and 84,837,646 Articles articles as of April 25, 2022.

The steps to download Z-Library books for free are as follows:

Step 1: Go to the Z-Library website ( https://singlelogin.me/ ) and Sign In.

Step 2: Browse through the categories or use the search bar to find the book you want.

Step 3: Click on the book to open it.

Step 4: Click on the download button to download the book.

4. Unpaywall

This is a huge database that contains more than 21 million academic works from over fifty thousand content repositories as well as publishers. The content in the database is replicated from government resources so downloading them is legal. The authors claim they are able to access around 80-85 percent of all scientific papers accessible on their website.

You can utilize Google’s Chrome extension to quickly get them at any time.

In order to do this, you have to follow the instructions listed below:

Visit https://unpaywall.org/products/extension
Select on the “Add the Chrome” button. Chrome” option.
Simply click “Add the store to Chrome” in the Chrome Web Store page in addition.
Keep an eye on the extension until it is installed.
After installing the extension, it will work automatically and will appear whenever you go to the site of a paywalled research paper in the database of Unpaywall’s open databases. All you have just click on the green Unpaywall button to allow the article to be displayed immediately.

5. Directory of Open Access Journals

The site was launched in 2003 with the aim of increasing the visibility of OA scholarly journals online. Content on the site covers subjects from science, to law, to fine arts, and everything in between. DOAJ has a commitment to “increase the visibility, accessibility, reputation, usage and impact of quality, peer-reviewed, OA scholarly research journals globally, regardless of discipline, geography or language.”

It can be used to search for and download research papers for free:

Visit: https://doaj.org/
Input your keywords in the search field , then hit enter.
Choose the research paper you wish to download.
Hit on the “Full Text” button that is located just below the abstract.

6.ScienceOpen

ScienceOpen offers a professional network platform for academics that gives access to more than 40 million research papers from all fields of science. Although you do need to register to view the full text of articles, registration is free. The advanced search function is highly detailed, allowing you to find exactly the research you’re looking for. You can also bookmark articles for later research. There are extensive networking options, including your Science Open profile, a forum for interacting with other researchers, the ability to track your usage and citations, and an interactive bibliography. Users have the ability to review articles and provide their knowledge and insight within the community.

To search for research papers with the help of Science open:

Go to: http://about.scienceopen.com/ .
Select on the “green “Search” button located in the upper right corner.
Enter your search terms into the search box. In addition to the keywords, you can look up authors’ collections, journals publishers, as well as others.

OA.mg is a search engine for academic papers. Whether you are looking for a specific paper, or for research from a field, or all of an author’s works – OA.mg is the place to find it. Research papers can be found by using OA.mg by following these steps:

Follow the link below: https://oa.mg
You can enter your keywords or DOI number into the search field that is available there.
Select on the “search” button, and wait for results to show up.
In the search results Download any research document you require by clicking this link for download.

8.Citationsy Archives

Citationsy Archives allows you to look up journals and papers to download, download them, and (obviously) incorporate them into your work.It is important to note that you can access Citationsy Archives with or without an account.

All you have to do is make a request, and it will then search for the exact phrase in all research papers around the world and show the pertinent matches to you. Click on each of them to view more information, and then access it directly from the search results.

The platform also allows you to download the papers using a number of different and totally open access and legal options.

Use Citationsy Archives from https://citationsy.com/archives/

CORE is the world’s largest aggregator of open access research papers from repositories and journals. It is a not-for-profit service dedicated to the open access mission. They serve the global network of repositories and journals by increasing the discoverability and reuse of open access content.

To find a research article using CORE:

Visit: https://core.ac.uk/
Enter your search terms into the search box.
Hit the “Search” link.
Select on the “Get PDF” button to download any research document you are looking for.

10. PaperPanda

PaperPanda is a Chrome extension that uses some clever logic and the Panda’s detective skills to find you the research paper PDFs you need. Essentially, when you activate PaperPanda it finds the DOI of the paper from the current page, and then goes and searches for it. It starts by querying various Open Access repositories like OpenAccessButton, OaDoi, SemanticScholar, Core, ArXiV, and the Internet Archive. You can also set your university libraries domain in the settings (this feature is in the works and coming soon). PaperPanda will then automatically search for the paper through your library. You can also set a different custom domain in the settings.

11.Dimensions

Dimensions covers millions of research publications connected by more than 1.6 billion citations, supporting grants, datasets, clinical trials, patents and policy documents. Dimensions is the most comprehensive research grants database which links grants to millions of resulting publications, clinical trials and patents.

Dimensions includes datasets from repositories such as Figshare, Dryad, Zenodo, Pangaea, and many more. It hosts millions of patents with links to other citing patents as well as to publications and supporting grants.

Visit: https://www.dimensions.ai/

https://www.scribendi.com/academy/articles/free_online_journal_and_research_databases.en.html

https://gauravtiwari.org/download-research-papers-for-free/

8 Sites to Download Research Papers for Free – 2020

https://microbiologynote.com/12-top-websites-to-download-research-papers-for-free/

14 Websites to Download Research Paper for Free – 2023

https://en.wikipedia.org/wiki/Library_Genesis

Z-Library is legal? You can Download 70,000,000+ scientific articles for free

Free Harvard Referencing Generator

Generate accurate Harvard reference lists quickly and for FREE, with MyBib!

🤔 What is a Harvard Referencing Generator?

A Harvard Referencing Generator is a tool that automatically generates formatted academic references in the Harvard style.

It takes in relevant details about a source -- usually critical information like author names, article titles, publish dates, and URLs -- and adds the correct punctuation and formatting required by the Harvard referencing style.

The generated references can be copied into a reference list or bibliography, and then collectively appended to the end of an academic assignment. This is the standard way to give credit to sources used in the main body of an assignment.

👩‍🎓 Who uses a Harvard Referencing Generator?

Harvard is the main referencing style at colleges and universities in the United Kingdom and Australia. It is also very popular in other English-speaking countries such as South Africa, Hong Kong, and New Zealand. University-level students in these countries are most likely to use a Harvard generator to aid them with their undergraduate assignments (and often post-graduate too).

🙌 Why should I use a Harvard Referencing Generator?

A Harvard Referencing Generator solves two problems:

It provides a way to organise and keep track of the sources referenced in the content of an academic paper.
It ensures that references are formatted correctly -- inline with the Harvard referencing style -- and it does so considerably faster than writing them out manually.

A well-formatted and broad bibliography can account for up to 20% of the total grade for an undergraduate-level project, and using a generator tool can contribute significantly towards earning them.

⚙️ How do I use MyBib's Harvard Referencing Generator?

Here's how to use our reference generator:

If citing a book, website, journal, or video: enter the URL or title into the search bar at the top of the page and press the search button.
Choose the most relevant results from the list of search results.
Our generator will automatically locate the source details and format them in the correct Harvard format. You can make further changes if required.
Then either copy the formatted reference directly into your reference list by clicking the 'copy' button, or save it to your MyBib account for later.

MyBib supports the following for Harvard style:

🍏 What other versions of Harvard referencing exist?

There isn't "one true way" to do Harvard referencing, and many universities have their own slightly different guidelines for the style. Our generator can adapt to handle the following list of different Harvard styles:

Cite Them Right
Manchester Metropolitan University (MMU)
University of the West of England (UWE)

Daniel is a qualified librarian, former teacher, and citation expert. He has been contributing to MyBib since 2018.

The green IT revolution: A blueprint for CIOs to combat climate change

Companies and governments looking to combat climate change are turning to tech for help. AI, new technologies, and some promising tech-driven business models have raised hopes for dramatic progress.

About the authors

This article is a collaborative effort by Gerrit Becker, Luca Bennici, Anamika Bhargava, Andrea Del Miglio , Jeffrey Lewis , and Pankaj Sachdeva, representing views from McKinsey Technology.

While many organizations’ climate goals are lofty, enterprise technology leaders—CIOs, chief digital innovation officers (CDIOs), and chief technology officers (CTOs), among others—have not always succeeded at turning climate ambitions into reality. One of the biggest reasons is that hard facts and clear paths of action are scarce. Misconceptions and misinformation have clouded the picture of what CIOs and tech leaders should do.

We have done extensive analysis of where technology can have the biggest impact on reducing emissions. To start, we divided technology’s role into two primary types of activities:

offense—the use of technology and analytics to cut emissions by reducing (improving operational efficiency), replacing (shifting emission-generating activities to cleaner alternatives), and reusing (recycling material)
defense—the actions IT can take to reduce emissions from the enterprise’s technology estate

Scope of the McKinsey analysis

McKinsey’s emissions analysis for this report focuses on enterprise technology emissions, which are the business IT emissions from the hardware, software, IT services, enterprise communications equipment, mobile devices, fixed and mobile network services, and internal technology teams that a company uses for its own operations and that a CIO has control over. These include the emissions related to the full life cycles of the products and services that an enterprise IT function uses, including their development, delivery, usage, and end of life (exhibit). Our internal services emissions' analysis assumes around 40 percent of IT workers are working from home.

The analysis does not include the emissions from the technology products and services that a company is selling (such as data center capacity sold by hyperscalers), operational technology devices (such as sensors and point-of-sale systems), and cryptocurrency mining.

The defense activities are where the CIO, as the head of IT, can act independently and quickly. This article focuses on defense, specifically the IT elements over which a CIO has direct control. We examined emissions from use of electricity for owned enterprise IT operations, such as the running of on-premises data centers and devices (classified as scope 2 by the Greenhouse Gas Protocol 1 Greenhouse Gas Protocol: Technical Guidance for Calculating Scope 3 Emissions: Supplement to the Corporate Value Chain (Scope 3) Accounting & Reporting Standard , World Resources Institute & World Business Council for Sustainable Development, 2013. Scope 1 emissions are direct emissions from the activities of an organization or under their control, including fuel combustion on site such as gas boilers, fleet vehicles, and air-conditioning leaks; scope 2 emissions are from electricity purchased and used by the organization; and scope 3 emissions are all indirect emissions not included in scope 2 that occur in the value chain of the reporting company, including both upstream and downstream emissions. ), and indirect emissions from technology devices that the CIO buys and disposes of (scope 3). 2 These calculations do not include emissions from technology-driven services sold, such as cloud capacity. (See sidebar, “Scope of the McKinsey analysis.”)

What the facts say

Our analysis has uncovered several facts that contravene some commonly held views about enterprise technology emissions. These facts involve the significant amount of tech-related emissions, the share of emissions from end-user devices, the variety of mitigation options available, and the favorable impact of shifting to cloud computing.

Enterprise technology generates significant emissions

Enterprise technology is responsible for emitting about 350 to 400 megatons of carbon dioxide equivalent gases (CO 2 e), accounting for about 1 percent of total global greenhouse gas (GHG) emissions. At first blush, this might not seem like a lot, but it equals about half of the emissions from aviation or shipping and is the equivalent of the total carbon emitted by the United Kingdom.

The industry sector that contributes the largest share of technology-related scope 2 and scope 3 GHG emissions is communications, media, and services (Exhibit 1). Enterprise technology’s contribution to total emissions is especially high for insurance (45 percent of total scope 2 emissions) and for banking and investment services (36 percent).

This amount of carbon dioxide and equivalent gases is a significant prize for companies under increasing pressure to cut emissions. Progress on climate change requires action on many fronts, and enterprise technology offers an important option that CIOs and companies can act on quickly.

Taking a photo of bamboo forest - stock photo

You’re invited

To a McKinsey Technology webinar on the critical role of technology in building a sustainable enterprise on October 25, 9:30–10:30am ET.

The biggest carbon culprit is end-user devices, not on-premises data centers

End-user devices—laptops, tablets, smartphones, and printers—generate 1.5 to 2.0 times more carbon globally than data centers (Exhibit 2). 3 On-premises and co-located data centers used by enterprises, not including data center capacity sold by hyperscalers. One reason is that companies have significantly more end-user devices than servers in on-premises data centers. In addition, the devices typically are replaced much more often: smartphones have an average refresh cycle of two years, laptops four years, and printers five years. On average, servers are replaced every five years, though 19 percent of organizations wait longer. 4 Rhona Ascierto and Andy Lawrence, Uptime Institute global data center survey 2020 , Uptime Institute, July 2020.

More worrisome, emissions from end-user devices are on track to increase at a CAGR of 12.8 percent per year. 5 End-user computing market: Growth, trends, COVID-19 impact, and forecasts (2022–2027) , Mordor Intelligence, January 2022. Efforts to address this could target the major causes of emissions from these devices. About three-fourths of the emissions comes from manufacturing, upstream transportation, and disposal. A significant source of these emissions is the semiconductors that power the devices.

Plenty of low-cost/high-impact options exist, starting with improved sourcing

We have found that when it comes to going green, many CIOs think in terms of investments needed to replace items or upgrade facilities. Our analysis, however, finds that CIOs can capture significant carbon benefits without making a significant investment—and in some cases can even save money (Exhibit 3).

Overall, for example, 50 to 60 percent of emissions related to end-user devices can be addressed through sourcing changes, primarily by procuring fewer devices per person and extending the life cycle of each device through recycling. These options will not require any investment and will lower costs, though companies may want to evaluate the impact on employee experience.

In addition, companies can more aggressively recycle their devices; 89 percent of organizations recycle less than 10 percent of their hardware overall. 6 Sustainable IT: Why it’s time for a green revolution for your organization’s IT , Capgemini Research Institute, 2021. CIOs can put pressure on suppliers to use greener devices, especially as companies in the semiconductor sector are already increasing their commitments to emission reduction. Further low-cost, high-impact actions include optimizing business travel and data center computing needs, as well as increasing the use of cloud to manage workloads.

Moving to cloud has more impact than optimizing data centers

Optimizing an on-premises data center’s power usage effectiveness (PUE) 7 PUE describes how efficiently a computer data center uses energy, expressed as the ratio of total facility energy to IT equipment energy. is expensive and results in limited carbon abatement. If a company were to double what it spends on infrastructure and cloud to reduce PUE, it would cut carbon emissions by only 15 to 20 percent. Structural improvements in data centers and optimized layout can help, but the impact is limited, and many companies have already implemented them. More aggressive measures, such as moving data centers to cooler locations or investing in new cooling tech, are prohibitively expensive.

A more effective approach is to migrate workloads to the cloud. Hyperscalers (also known as cloud service providers) and co-locators are investing significantly to become greener through measures such as buying green energy themselves and investing in ultra-efficient data centers with a PUE equal to or less than 1.10, compared with the average PUE of 1.57 for an on-premises data center. 8 “Uptime Institute 11th annual Global Data Center Survey shows sustainability, outage, and efficiency challenges amid capacity growth,” Uptime Institute, September 14, 2021. (We estimate that companies could achieve just a 1.3 PUE score for their data center if they invested nearly 250 percent more, on average, over what they currently spend for their data centers and cloud presence.)

With thoughtful migration to and optimized usage of the cloud, companies could reduce the carbon emissions from their data centers by more than 55 percent—about 40 megatons of CO 2 e worldwide, the equivalent of the total carbon emissions from Switzerland.

Three steps to take now

With companies and governments under intensifying pressure to cut carbon emissions and with technology playing a key role in delivering on those goals, CIOs will find themselves on the front lines. The challenge will be to reduce IT’s carbon footprint while delivering high-quality, low-cost technology services to customers and employees.

On average, completion of the defensive steps might take three to four years. However, CIOs who act decisively and precisely can achieve 15 to 20 percent of carbon reduction potential in the first year with minimal investment.

CIOs can choose from among a wide array responses, particularly in conjunction with the CEO and the board. However, three measures they can take right now will prepare the organization for longer-term efforts. These measures involve sourcing strategies, key metrics, and a performance management system.

The net-zero transition: What it would cost, what it could bring

Move now on sourcing strategies.

Far and away the fastest and most effective defensive measure for reducing IT carbon emissions is to revise policies for technology sourcing. Optimizing the number of devices in line with standards followed by companies in the top quartile 9 Top quartile in terms of the ratio of devices to people is derived from the number of devices per person. Our analysis uses McKinsey Digital’s Ignite solutions and 2020 data. would reduce about 30 percent of end-user-device emissions, the amount of carbon emitted by Hong Kong. For example, top-quartile companies have one printer for every 16 people in the workplace; the overall average is one printer per eight people.

This sourcing shift does not necessarily lead to a degradation in user experience, because the rollout of 5G and increasingly advanced processing and compute power allow the main processing function to happen at the server. Therefore, devices can be less powerful and consume much less energy. Essentially, this is a software-as-a-service (SaaS) model where high-end and user-friendly experiences happen on the server, not the device. The effectiveness of this approach will depend on having stable networks, less resource-intensive coding at the device level, edge computing capabilities, and shifts of offerings to more efficient platforms (for example, cloud).

As part of this effort, the CIO and the business’s head of procurement will need to collaborate on reviewing and adjusting device refresh timelines and device-to-person ratios, as well as adjusting the basis for purchasing decisions. Procurement generally relies on cost/benefit calculations, and rightly so. That approach will need to expand to account for carbon dioxide emissions. The spirit of collaboration should extend to suppliers as well, with the parties working together to formulate plans that provide the greatest benefits for all.

A more thoughtful sourcing strategy extends beyond end-user devices. CIOs, for example, should look for green sources of the electricity IT uses. When these sources are unavailable, CIOs can direct procurement to power purchase agreements to offset carbon use. CIOs can also set green standards for their vendors and suppliers, requiring GHG emissions disclosures and incorporating them into their criteria for purchase decisions.

Establish a green ROI metric for technology costs

Any real progress on green technology can happen only when companies measure their “green returns.” But today, most green metrics omit cost and savings, which ultimately makes them impractical. A better metric focuses on cost per ton of carbon saved (accounting for costs saved as well). Sophisticated models calculate emissions throughout the full life cycle, including production, transportation, and disposal.

CIOs can further assess suppliers, manufacturers, and service providers based on how advanced they are in recycling and refurbishing electronics; designing circular components; extending product life cycles with better design, higher-quality manufacturing, and more robust materials; offering repair services; and reselling to consumers.

Decisions about IT spending need to consider a range of factors, including technical debt abatement and business strategy. Along with these factors, companies should institutionalize a green ROI metric that is transparent to everybody in the business as an element in IT decision making, including in requests for proposals (RFPs). Doing so will enable companies to better understand the true impact their technology is having on carbon emissions.

Put in place green measurement systems

Establishing a green ROI metric is only a start. CIOs need to establish a baseline of performance, measure progress against the baseline, and track impact in near real time, much as companies track real-time computer and network usage for applications in the cloud. This kind of measuring system ensures that CIOs know what’s working and what isn’t, so they can adjust quickly.

In practice, implementing green measurement can be challenging. Some companies have spent a year measuring their carbon footprint, ending up with an outdated analysis. This tends to happen when companies are determined to measure every bit of carbon emitted, a praiseworthy but time-consuming effort. CIOs can make substantial progress by instead prioritizing measurement where the impact is highest, such as tracking the number of end-user devices purchased and in use, the current duration of use for each device, and the ratio of devices per user. Another way CIOs can make quick progress is to embed emissions- and power-monitoring capabilities into large technology assets and work with external providers, such as electricity companies, to track usage in real time.

Effectively combating climate change won’t happen through one or two big wins; those don’t exist yet. To have real impact, companies and governments will need to act in many areas. Technology has a huge role to play in many of these areas, but CIOs and tech leaders need to act quickly and decisively.

This article is the first in a series about how CIOs can reduce emissions. The next article will explore how CIOs can drive the business’s sustainability agenda by playing offense and implementing reduce, replace, and reuse levers to decarbonize.

Gerrit Becker is an associate partner in McKinsey’s Frankfurt office, Luca Bennici is an associate partner in the Dubai office, Anamika Bhargava is a consultant in the Toronto office, Andrea Del Miglio is a senior partner in the Milan office, Jeffrey Lewis is a senior partner in the New Jersey office, and Pankaj Sachdeva is a partner in the Philadelphia office.

The authors wish to thank Bernardo Betley, Arjita Bhan, Raghuvar Choppakatla, Sebastian Hoffmann, Abdelrahman Mahfouz, Tom Pütz, Jürgen Sailer, Tim Vroman, Alice Yu, and Gisella Zapata for their contributions to this article.

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How to Download Research Papers/Journals for Free

A detailed guide on How to download research papers for free.

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Published: 14 May 2024

2023 summer warmth unparalleled over the past 2,000 years

Jan Esper ORCID: orcid.org/0000-0003-3919-014X 1 , 2 ,
Max Torbenson ORCID: orcid.org/0000-0003-2720-2238 1 &
Ulf Büntgen 2 , 3 , 4

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Climate change
Palaeoclimate

Including an exceptionally warm Northern Hemisphere (NH) summer 1 ,2 , 2023 has been reported as the hottest year on record 3-5 . Contextualizing recent anthropogenic warming against past natural variability is nontrivial, however, because the sparse 19 th century meteorological records tend to be too warm 6 . Here, we combine observed and reconstructed June-August (JJA) surface air temperatures to show that 2023 was the warmest NH extra-tropical summer over the past 2000 years exceeding the 95% confidence range of natural climate variability by more than half a degree Celsius. Comparison of the 2023 JJA warming against the coldest reconstructed summer in 536 CE reveals a maximum range of pre-Anthropocene-to-2023 temperatures of 3.93°C. Although 2023 is consistent with a greenhouse gases-induced warming trend 7 that is amplified by an unfolding El Niño event 8 , this extreme emphasizes the urgency to implement international agreements for carbon emission reduction.

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Esper, J., Torbenson, M. & Büntgen, U. 2023 summer warmth unparalleled over the past 2,000 years. Nature (2024). https://doi.org/10.1038/s41586-024-07512-y

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