Machine Learning - CMU
PhD Dissertations
[all are .pdf files].
Neural processes underlying cognitive control during language production (unavailable) Tara Pirnia, 2024
The Neurodynamic Basis of Real World Face Perception Arish Alreja, 2024
Towards More Powerful Graph Representation Learning Lingxiao Zhao, 2024
Robust Machine Learning: Detection, Evaluation and Adaptation Under Distribution Shift Saurabh Garg, 2024
UNDERSTANDING, FORMALLY CHARACTERIZING, AND ROBUSTLY HANDLING REAL-WORLD DISTRIBUTION SHIFT Elan Rosenfeld, 2024
Representing Time: Towards Pragmatic Multivariate Time Series Modeling Cristian Ignacio Challu, 2024
Foundations of Multisensory Artificial Intelligence Paul Pu Liang, 2024
Advancing Model-Based Reinforcement Learning with Applications in Nuclear Fusion Ian Char, 2024
Learning Models that Match Jacob Tyo, 2024
Improving Human Integration across the Machine Learning Pipeline Charvi Rastogi, 2024
Reliable and Practical Machine Learning for Dynamic Healthcare Settings Helen Zhou, 2023
Automatic customization of large-scale spiking network models to neuronal population activity (unavailable) Shenghao Wu, 2023
Estimation of BVk functions from scattered data (unavailable) Addison J. Hu, 2023
Rethinking object categorization in computer vision (unavailable) Jayanth Koushik, 2023
Advances in Statistical Gene Networks Jinjin Tian, 2023 Post-hoc calibration without distributional assumptions Chirag Gupta, 2023
The Role of Noise, Proxies, and Dynamics in Algorithmic Fairness Nil-Jana Akpinar, 2023
Collaborative learning by leveraging siloed data Sebastian Caldas, 2023
Modeling Epidemiological Time Series Aaron Rumack, 2023
Human-Centered Machine Learning: A Statistical and Algorithmic Perspective Leqi Liu, 2023
Uncertainty Quantification under Distribution Shifts Aleksandr Podkopaev, 2023
Probabilistic Reinforcement Learning: Using Data to Define Desired Outcomes, and Inferring How to Get There Benjamin Eysenbach, 2023
Comparing Forecasters and Abstaining Classifiers Yo Joong Choe, 2023
Using Task Driven Methods to Uncover Representations of Human Vision and Semantics Aria Yuan Wang, 2023
Data-driven Decisions - An Anomaly Detection Perspective Shubhranshu Shekhar, 2023
Applied Mathematics of the Future Kin G. Olivares, 2023
METHODS AND APPLICATIONS OF EXPLAINABLE MACHINE LEARNING Joon Sik Kim, 2023
NEURAL REASONING FOR QUESTION ANSWERING Haitian Sun, 2023
Principled Machine Learning for Societally Consequential Decision Making Amanda Coston, 2023
Long term brain dynamics extend cognitive neuroscience to timescales relevant for health and physiology Maxwell B. Wang, 2023
Long term brain dynamics extend cognitive neuroscience to timescales relevant for health and physiology Darby M. Losey, 2023
Calibrated Conditional Density Models and Predictive Inference via Local Diagnostics David Zhao, 2023
Towards an Application-based Pipeline for Explainability Gregory Plumb, 2022
Objective Criteria for Explainable Machine Learning Chih-Kuan Yeh, 2022
Making Scientific Peer Review Scientific Ivan Stelmakh, 2022
Facets of regularization in high-dimensional learning: Cross-validation, risk monotonization, and model complexity Pratik Patil, 2022
Active Robot Perception using Programmable Light Curtains Siddharth Ancha, 2022
Strategies for Black-Box and Multi-Objective Optimization Biswajit Paria, 2022
Unifying State and Policy-Level Explanations for Reinforcement Learning Nicholay Topin, 2022
Sensor Fusion Frameworks for Nowcasting Maria Jahja, 2022
Equilibrium Approaches to Modern Deep Learning Shaojie Bai, 2022
Towards General Natural Language Understanding with Probabilistic Worldbuilding Abulhair Saparov, 2022
Applications of Point Process Modeling to Spiking Neurons (Unavailable) Yu Chen, 2021
Neural variability: structure, sources, control, and data augmentation Akash Umakantha, 2021
Structure and time course of neural population activity during learning Jay Hennig, 2021
Cross-view Learning with Limited Supervision Yao-Hung Hubert Tsai, 2021
Meta Reinforcement Learning through Memory Emilio Parisotto, 2021
Learning Embodied Agents with Scalably-Supervised Reinforcement Learning Lisa Lee, 2021
Learning to Predict and Make Decisions under Distribution Shift Yifan Wu, 2021
Statistical Game Theory Arun Sai Suggala, 2021
Towards Knowledge-capable AI: Agents that See, Speak, Act and Know Kenneth Marino, 2021
Learning and Reasoning with Fast Semidefinite Programming and Mixing Methods Po-Wei Wang, 2021
Bridging Language in Machines with Language in the Brain Mariya Toneva, 2021
Curriculum Learning Otilia Stretcu, 2021
Principles of Learning in Multitask Settings: A Probabilistic Perspective Maruan Al-Shedivat, 2021
Towards Robust and Resilient Machine Learning Adarsh Prasad, 2021
Towards Training AI Agents with All Types of Experiences: A Unified ML Formalism Zhiting Hu, 2021
Building Intelligent Autonomous Navigation Agents Devendra Chaplot, 2021
Learning to See by Moving: Self-supervising 3D Scene Representations for Perception, Control, and Visual Reasoning Hsiao-Yu Fish Tung, 2021
Statistical Astrophysics: From Extrasolar Planets to the Large-scale Structure of the Universe Collin Politsch, 2020
Causal Inference with Complex Data Structures and Non-Standard Effects Kwhangho Kim, 2020
Networks, Point Processes, and Networks of Point Processes Neil Spencer, 2020
Dissecting neural variability using population recordings, network models, and neurofeedback (Unavailable) Ryan Williamson, 2020
Predicting Health and Safety: Essays in Machine Learning for Decision Support in the Public Sector Dylan Fitzpatrick, 2020
Towards a Unified Framework for Learning and Reasoning Han Zhao, 2020
Learning DAGs with Continuous Optimization Xun Zheng, 2020
Machine Learning and Multiagent Preferences Ritesh Noothigattu, 2020
Learning and Decision Making from Diverse Forms of Information Yichong Xu, 2020
Towards Data-Efficient Machine Learning Qizhe Xie, 2020
Change modeling for understanding our world and the counterfactual one(s) William Herlands, 2020
Machine Learning in High-Stakes Settings: Risks and Opportunities Maria De-Arteaga, 2020
Data Decomposition for Constrained Visual Learning Calvin Murdock, 2020
Structured Sparse Regression Methods for Learning from High-Dimensional Genomic Data Micol Marchetti-Bowick, 2020
Towards Efficient Automated Machine Learning Liam Li, 2020
LEARNING COLLECTIONS OF FUNCTIONS Emmanouil Antonios Platanios, 2020
Provable, structured, and efficient methods for robustness of deep networks to adversarial examples Eric Wong , 2020
Reconstructing and Mining Signals: Algorithms and Applications Hyun Ah Song, 2020
Probabilistic Single Cell Lineage Tracing Chieh Lin, 2020
Graphical network modeling of phase coupling in brain activity (unavailable) Josue Orellana, 2019
Strategic Exploration in Reinforcement Learning - New Algorithms and Learning Guarantees Christoph Dann, 2019 Learning Generative Models using Transformations Chun-Liang Li, 2019
Estimating Probability Distributions and their Properties Shashank Singh, 2019
Post-Inference Methods for Scalable Probabilistic Modeling and Sequential Decision Making Willie Neiswanger, 2019
Accelerating Text-as-Data Research in Computational Social Science Dallas Card, 2019
Multi-view Relationships for Analytics and Inference Eric Lei, 2019
Information flow in networks based on nonstationary multivariate neural recordings Natalie Klein, 2019
Competitive Analysis for Machine Learning & Data Science Michael Spece, 2019
The When, Where and Why of Human Memory Retrieval Qiong Zhang, 2019
Towards Effective and Efficient Learning at Scale Adams Wei Yu, 2019
Towards Literate Artificial Intelligence Mrinmaya Sachan, 2019
Learning Gene Networks Underlying Clinical Phenotypes Under SNP Perturbations From Genome-Wide Data Calvin McCarter, 2019
Unified Models for Dynamical Systems Carlton Downey, 2019
Anytime Prediction and Learning for the Balance between Computation and Accuracy Hanzhang Hu, 2019
Statistical and Computational Properties of Some "User-Friendly" Methods for High-Dimensional Estimation Alnur Ali, 2019
Nonparametric Methods with Total Variation Type Regularization Veeranjaneyulu Sadhanala, 2019
New Advances in Sparse Learning, Deep Networks, and Adversarial Learning: Theory and Applications Hongyang Zhang, 2019
Gradient Descent for Non-convex Problems in Modern Machine Learning Simon Shaolei Du, 2019
Selective Data Acquisition in Learning and Decision Making Problems Yining Wang, 2019
Anomaly Detection in Graphs and Time Series: Algorithms and Applications Bryan Hooi, 2019
Neural dynamics and interactions in the human ventral visual pathway Yuanning Li, 2018
Tuning Hyperparameters without Grad Students: Scaling up Bandit Optimisation Kirthevasan Kandasamy, 2018
Teaching Machines to Classify from Natural Language Interactions Shashank Srivastava, 2018
Statistical Inference for Geometric Data Jisu Kim, 2018
Representation Learning @ Scale Manzil Zaheer, 2018
Diversity-promoting and Large-scale Machine Learning for Healthcare Pengtao Xie, 2018
Distribution and Histogram (DIsH) Learning Junier Oliva, 2018
Stress Detection for Keystroke Dynamics Shing-Hon Lau, 2018
Sublinear-Time Learning and Inference for High-Dimensional Models Enxu Yan, 2018
Neural population activity in the visual cortex: Statistical methods and application Benjamin Cowley, 2018
Efficient Methods for Prediction and Control in Partially Observable Environments Ahmed Hefny, 2018
Learning with Staleness Wei Dai, 2018
Statistical Approach for Functionally Validating Transcription Factor Bindings Using Population SNP and Gene Expression Data Jing Xiang, 2017
New Paradigms and Optimality Guarantees in Statistical Learning and Estimation Yu-Xiang Wang, 2017
Dynamic Question Ordering: Obtaining Useful Information While Reducing User Burden Kirstin Early, 2017
New Optimization Methods for Modern Machine Learning Sashank J. Reddi, 2017
Active Search with Complex Actions and Rewards Yifei Ma, 2017
Why Machine Learning Works George D. Montañez , 2017
Source-Space Analyses in MEG/EEG and Applications to Explore Spatio-temporal Neural Dynamics in Human Vision Ying Yang , 2017
Computational Tools for Identification and Analysis of Neuronal Population Activity Pengcheng Zhou, 2016
Expressive Collaborative Music Performance via Machine Learning Gus (Guangyu) Xia, 2016
Supervision Beyond Manual Annotations for Learning Visual Representations Carl Doersch, 2016
Exploring Weakly Labeled Data Across the Noise-Bias Spectrum Robert W. H. Fisher, 2016
Optimizing Optimization: Scalable Convex Programming with Proximal Operators Matt Wytock, 2016
Combining Neural Population Recordings: Theory and Application William Bishop, 2015
Discovering Compact and Informative Structures through Data Partitioning Madalina Fiterau-Brostean, 2015
Machine Learning in Space and Time Seth R. Flaxman, 2015
The Time and Location of Natural Reading Processes in the Brain Leila Wehbe, 2015
Shape-Constrained Estimation in High Dimensions Min Xu, 2015
Spectral Probabilistic Modeling and Applications to Natural Language Processing Ankur Parikh, 2015 Computational and Statistical Advances in Testing and Learning Aaditya Kumar Ramdas, 2015
Corpora and Cognition: The Semantic Composition of Adjectives and Nouns in the Human Brain Alona Fyshe, 2015
Learning Statistical Features of Scene Images Wooyoung Lee, 2014
Towards Scalable Analysis of Images and Videos Bin Zhao, 2014
Statistical Text Analysis for Social Science Brendan T. O'Connor, 2014
Modeling Large Social Networks in Context Qirong Ho, 2014
Semi-Cooperative Learning in Smart Grid Agents Prashant P. Reddy, 2013
On Learning from Collective Data Liang Xiong, 2013
Exploiting Non-sequence Data in Dynamic Model Learning Tzu-Kuo Huang, 2013
Mathematical Theories of Interaction with Oracles Liu Yang, 2013
Short-Sighted Probabilistic Planning Felipe W. Trevizan, 2013
Statistical Models and Algorithms for Studying Hand and Finger Kinematics and their Neural Mechanisms Lucia Castellanos, 2013
Approximation Algorithms and New Models for Clustering and Learning Pranjal Awasthi, 2013
Uncovering Structure in High-Dimensions: Networks and Multi-task Learning Problems Mladen Kolar, 2013
Learning with Sparsity: Structures, Optimization and Applications Xi Chen, 2013
GraphLab: A Distributed Abstraction for Large Scale Machine Learning Yucheng Low, 2013
Graph Structured Normal Means Inference James Sharpnack, 2013 (Joint Statistics & ML PhD)
Probabilistic Models for Collecting, Analyzing, and Modeling Expression Data Hai-Son Phuoc Le, 2013
Learning Large-Scale Conditional Random Fields Joseph K. Bradley, 2013
New Statistical Applications for Differential Privacy Rob Hall, 2013 (Joint Statistics & ML PhD)
Parallel and Distributed Systems for Probabilistic Reasoning Joseph Gonzalez, 2012
Spectral Approaches to Learning Predictive Representations Byron Boots, 2012
Attribute Learning using Joint Human and Machine Computation Edith L. M. Law, 2012
Statistical Methods for Studying Genetic Variation in Populations Suyash Shringarpure, 2012
Data Mining Meets HCI: Making Sense of Large Graphs Duen Horng (Polo) Chau, 2012
Learning with Limited Supervision by Input and Output Coding Yi Zhang, 2012
Target Sequence Clustering Benjamin Shih, 2011
Nonparametric Learning in High Dimensions Han Liu, 2010 (Joint Statistics & ML PhD)
Structural Analysis of Large Networks: Observations and Applications Mary McGlohon, 2010
Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy Brian D. Ziebart, 2010
Tractable Algorithms for Proximity Search on Large Graphs Purnamrita Sarkar, 2010
Rare Category Analysis Jingrui He, 2010
Coupled Semi-Supervised Learning Andrew Carlson, 2010
Fast Algorithms for Querying and Mining Large Graphs Hanghang Tong, 2009
Efficient Matrix Models for Relational Learning Ajit Paul Singh, 2009
Exploiting Domain and Task Regularities for Robust Named Entity Recognition Andrew O. Arnold, 2009
Theoretical Foundations of Active Learning Steve Hanneke, 2009
Generalized Learning Factors Analysis: Improving Cognitive Models with Machine Learning Hao Cen, 2009
Detecting Patterns of Anomalies Kaustav Das, 2009
Dynamics of Large Networks Jurij Leskovec, 2008
Computational Methods for Analyzing and Modeling Gene Regulation Dynamics Jason Ernst, 2008
Stacked Graphical Learning Zhenzhen Kou, 2007
Actively Learning Specific Function Properties with Applications to Statistical Inference Brent Bryan, 2007
Approximate Inference, Structure Learning and Feature Estimation in Markov Random Fields Pradeep Ravikumar, 2007
Scalable Graphical Models for Social Networks Anna Goldenberg, 2007
Measure Concentration of Strongly Mixing Processes with Applications Leonid Kontorovich, 2007
Tools for Graph Mining Deepayan Chakrabarti, 2005
Automatic Discovery of Latent Variable Models Ricardo Silva, 2005
- Regression (Psychology)
Machine Learning -Regression
- January 2022
- Thesis for: 4th year seminar
- Advisor: Jamil layous
- Higher Institute for Applied Sciences and Technology
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Based on this background, the aim of this thesis is to select and implement a machine learning process that produces an algorithm, which is able to detect whether documents have been translated by humans or computerized systems. This algorithm builds the basic structure for an approach to evaluate these documents. 1.2 Related Work
that a machine can be made to simulate it." [3] In the AI field, there are several terms. Artificial intelligence is the largest collection, machine learning is a subset of artificial intelligence, and deep learning is a subset of machine learning, as shown in Exhibit 2.3 [4]. This thesis mainly
Machine learning (ML) systems are remarkably successful on a variety of benchmarks across sev-eral domains. In these benchmarks, the test data points, though not identical, are very similar to ... This thesis focuses on an extreme version of this brittleness, adversarial examples, where even imperceptible (but carefully constructed) changes ...
This thesis aims to help researchers and industry leaders understand how to implement machine learning (ML) as an early detection tool for anomalies (cyberattacks being a subset of anomalies) in their processes. With learnings from an end-to-end implementation of some stateart machine learning models and a -of-the-
of Machine Learning The initial version of this textbook was created by William J. Deuschle for his senior thesis, based on his notes of CS181 during the Spring of 2017. This textbook has since been maintained by the CS181 course staff with bug fixes from many CS181 students.
Machine learning applications to both decision-making and decision-support are growing. Further,witheachsuccessfulapplication,learningalgorithmsaregain- ing increased autonomy and control over decision-making. As a result, research into intelligent decision-making algorithms continues to improve. For example, theStanfordResearchInstitute ...
course and train a machine learning model to answer these questions. Our system demonstrates an overall accuracy of 96% for open-response questions and 97% for multiple-choice questions, compared with MIT students' average of 93%, achieving grade A performance in the course, all in real-time. Questions cover all 12 topics
Machine Learning, a natural outgrowth at the intersection of Computer Science and Statistics, has evolved into a broad, highly successful, and extremely dynamic discipline. ... In this thesis, we develop theoretical foundations and new algorithms for several important emerging learning paradigms of significant practical importance, including ...
and psychologists study learning in animals and humans. In this book we fo-cus on learning in machines. There are several parallels between animal and machine learning. Certainly, many techniques in machine learning derive from the e orts of psychologists to make more precise their theories of animal and human learning through computational models.
This thesis is the result of the graduation project of the Computer Science and Engineering pro-gram at the Eindhoven University of Technology (TU/e). The research of this project is performed within the Data Mining Group of the TU/e in collaboration with OpenML. I would like to thank my supervisor Joaquin Vanschoren.
In this thesis, we develop novel methods to show how longitudinal health data contained in Electronic Health Records (EHRs) can be harnessed for making novel clinical discoveries. ... this thesis illustrates how the use of machine learning for analyzing these large scale digital patient data repositories can yield new clinical discoveries and ...
Abstract. This thesis is about assessing the quality of technical texts such as user manuals and product speci cations. This is done by consulting industry standards and guidelines, and implementing an automatic extractor for features describing the texts, based on these guidelines. These features are then put together into models, which are ...
This thesis shows, drawing from a recent project at Nissan's Canton, ... Machine learning was not initially a part of our project scope. Two things led us to it. The first was the general frustration we heard from operators, engineers, and managers about the challenges they had dealing with data. With the number
Selecting the best machine learning algorithm for a problem is of paramount importance; choosing the correct one can be the di erence between the suc-cess and failure of a project. The goal of this research is to better de ne the approach to take when inspecting the di erences between machine learning techniques as applied to a particular task.
PhD Dissertations [All are .pdf files] Neural processes underlying cognitive control during language production (unavailable) Tara Pirnia, 2024 The Neurodynamic Basis of Real World Face Perception Arish Alreja, 2024. Towards More Powerful Graph Representation Learning Lingxiao Zhao, 2024. Robust Machine Learning: Detection, Evaluation and Adaptation Under Distribution Shift Saurabh Garg, 2024
Computer graphics, Machine learning, Deep Learning, Convolutional neural net-works, Object Classication, Synthetic data, Three.js Abstract One of the limitations of supervised learning in deep learning algorithm is to gather and label a large set of data. In this document, the approach to solve this limitation is presented by using synthetic data.
challenges that can be framed as learning problems. The performance of machine learning models depends on algorithms and the data. Moreover, learning algorithms create a model of reality through learning and testing with data processes, and their performance shows an agreement degree of their assumed model with reality.
2.1 Supervised Machine Learning In the scope of this thesis we focus only on the supervised learning. Two kinds of problems that supervised machine learning aims to solve are classification prob-lems and regression problems. Kevin P. Murphy in 2012 [1] defined the goal of classification as learning a mapping
Student PerformanceM. Sc. Thesis, 35 pages June 2017This thesis examines the application of machine learning algorithms t. predict whether a student will be successful or not. The specific focus of the thesis is the comparison of machine learning methods and feature engineering techniques in term. of how much they improve the prediction ...
Cataloged from PDF version of thesis. Includes bibliographical references (pages 357-372). by Alexander James Stimpson. Ph. D. ... and one of the most promising areas to prepare for this influx of complex training data is the field of machine learning. Thus, the objective of this thesis was to lay the foundations for the use of machine learning ...
The goal of a regression model is to build a mathematical equation that defines y. (the outcome v ariable) as a function of one or multiple predictor variables (x). Next, this equation can be used ...
intelligence and machine learning. This thesis will define machine learning and artificial intelligence for the investor and real estate audience, examine the ways in which these new analytic, predictive, and automating technologies are being used in the real estate industry, and postulate potential
ment process, from the preparation of data to the creation. 1: The workflow of a machine learning process [10].2.1.2 Over and underfittingThe goal with a machine learning model is to be able to generalize f. om training data, and apply predictions to new data in the same problem domain. Overfitting and underfitting.