[All are .pdf files]

Adapting to Structure and Using Structure to Adapt: Toward Explaining the Success of Modern Deep Learning
Stefani Karp, 2024

GUIDING MACHINE LEARNING DESIGN WITH INSIGHTS FROM SIMPLE SANDBOXES
Bingbin Liu, 2024

Generative Models for Structured Discrete Data with Application to Drug Discovery
Chenghui Zhou, 2024

The Dynamics of Optimization in Deep Learning
Jeremy M. Cohen, 2024

New perspectives on optimization: combating data poisoning, solving Euclidean optimization and learning minimax optimal estimators
Kartik Gupta, 2024

Computational Exploration of Higher Visual Selectivity in the Human Brain
Andrew Luo, 2024

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 andphysiology
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

Accelerating Text-as-Data Research in Computational Social Science
Dallas Card, 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