ML Anthology

Foster, Dylan J.

58 publications

COLT 2025 Computational-Statistical Tradeoffs at the Next-Token Prediction Barrier: Autoregressive and Imitation Learning Under Misspecification (extended Abstract) Dhruv Rohatgi, Adam Block, Audrey Huang, Akshay Krishnamurthy, Dylan J. Foster
ICLR 2025 Correcting the Mythos of KL-Regularization: Direct Alignment Without Overoptimization via Chi-Squared Preference Optimization Audrey Huang, Wenhao Zhan, Tengyang Xie, Jason D. Lee, Wen Sun, Akshay Krishnamurthy, Dylan J Foster
ICLR 2025 Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF Tengyang Xie, Dylan J Foster, Akshay Krishnamurthy, Corby Rosset, Ahmed Hassan Awadallah, Alexander Rakhlin
ICML 2025 Is Best-of-N the Best of Them? Coverage, Scaling, and Optimality in Inference-Time Alignment Audrey Huang, Adam Block, Qinghua Liu, Nan Jiang, Akshay Krishnamurthy, Dylan J Foster
COLT 2025 Is a Good Foundation Necessary for Efficient Reinforcement Learning? the Computational Role of the Base Model in Exploration Dylan J Foster, Zakaria Mhammedi, Dhruv Rohatgi
COLT 2025 Necessary and Sufficient Oracles: Toward a Computational Taxonomy for Reinforcement Learning Dhruv Rohatgi, Dylan J. Foster
ICLR 2025 Self-Improvement in Language Models: The Sharpening Mechanism Audrey Huang, Adam Block, Dylan J Foster, Dhruv Rohatgi, Cyril Zhang, Max Simchowitz, Jordan T. Ash, Akshay Krishnamurthy
NeurIPS 2024 Assouad, Fano, and Le Cam with Interaction: A Unifying Lower Bound Framework and Characterization for Bandit Learnability Fan Chen, Dylan J. Foster, Yanjun Han, Jian Qian, Alexander Rakhlin, Yunbei Xu
ICLR 2024 Butterfly Effects of SGD Noise: Error Amplification in Behavior Cloning and Autoregression Adam Block, Dylan J Foster, Akshay Krishnamurthy, Max Simchowitz, Cyril Zhang
NeurIPS 2024 Can Large Language Models Explore In-Context? Akshay Krishnamurthy, Keegan Harris, Dylan J. Foster, Cyril Zhang, Aleksandrs Slivkins
ICMLW 2024 Can Large Language Models Explore In-Context? Akshay Krishnamurthy, Keegan Harris, Dylan J Foster, Cyril Zhang, Aleksandrs Slivkins
JMLR 2024 Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regression Aleksandrs Slivkins, Xingyu Zhou, Karthik Abinav Sankararaman, Dylan J. Foster
ICLR 2024 Harnessing Density Ratios for Online Reinforcement Learning Philip Amortila, Dylan J Foster, Nan Jiang, Ayush Sekhari, Tengyang Xie
NeurIPS 2024 Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning Dylan J. Foster, Adam Block, Dipendra Misra
NeurIPS 2024 Online Estimation via Offline Estimation: An Information-Theoretic Framework Dylan J. Foster, Yanjun Han, Jian Qian, Alexander Rakhlin
NeurIPS 2024 Reinforcement Learning Under Latent Dynamics: Toward Statistical and Algorithmic Modularity Philip Amortila, Dylan J. Foster, Nan Jiang, Akshay Krishnamurthy, Zakaria Mhammedi
ICML 2024 Rich-Observation Reinforcement Learning with Continuous Latent Dynamics Yuda Song, Lili Wu, Dylan J Foster, Akshay Krishnamurthy
ICML 2024 Scalable Online Exploration via Coverability Philip Amortila, Dylan J Foster, Akshay Krishnamurthy
NeurIPSW 2024 Self-Improvement in Language Models: The Sharpening Mechanism Audrey Huang, Adam Block, Dylan J Foster, Dhruv Rohatgi, Cyril Zhang, Max Simchowitz, Jordan T. Ash, Akshay Krishnamurthy
NeurIPS 2024 The Power of Resets in Online Reinforcement Learning Zakaria Mhammedi, Dylan J. Foster, Alexander Rakhlin
COLT 2023 Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regression Aleksandrs Slivkins, Karthik Abinav Sankararaman, Dylan J Foster
NeurIPS 2023 Efficient Model-Free Exploration in Low-Rank MDPs Zak Mhammedi, Adam Block, Dylan J Foster, Alexander Rakhlin
TMLR 2023 Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models Alex Lamb, Riashat Islam, Yonathan Efroni, Aniket Rajiv Didolkar, Dipendra Misra, Dylan J Foster, Lekan P Molu, Rajan Chari, Akshay Krishnamurthy, John Langford
ICML 2023 Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games Dylan J Foster, Noah Golowich, Sham M. Kakade
COLT 2023 Instance-Optimality in Interactive Decision Making: Toward a Non-Asymptotic Theory Andrew J. Wagenmaker, Dylan J. Foster
NeurIPS 2023 Model-Free Reinforcement Learning with the Decision-Estimation Coefficient Dylan J Foster, Noah Golowich, Jian Qian, Alexander Rakhlin, Ayush Sekhari
COLT 2023 On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring Dean Foster, Dylan J. Foster, Noah Golowich, Alexander Rakhlin
ICML 2023 Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL Zakaria Mhammedi, Dylan J Foster, Alexander Rakhlin
ICLR 2023 The Role of Coverage in Online Reinforcement Learning Tengyang Xie, Dylan J Foster, Yu Bai, Nan Jiang, Sham M. Kakade
COLT 2023 Tight Guarantees for Interactive Decision Making with the Decision-Estimation Coefficient Dylan J. Foster, Noah Golowich, Yanjun Han
ICML 2022 Contextual Bandits with Large Action Spaces: Made Practical Yinglun Zhu, Dylan J Foster, John Langford, Paul Mineiro
NeurIPS 2022 Interaction-Grounded Learning with Action-Inclusive Feedback Tengyang Xie, Akanksha Saran, Dylan J Foster, Lekan Molu, Ida Momennejad, Nan Jiang, Paul Mineiro, John Langford
COLT 2022 Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation Dylan J Foster, Akshay Krishnamurthy, David Simchi-Levi, Yunzong Xu
NeurIPS 2022 On the Complexity of Adversarial Decision Making Dylan J Foster, Alexander Rakhlin, Ayush Sekhari, Karthik Sridharan
COLT 2022 Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information Yonathan Efroni, Dylan J Foster, Dipendra Misra, Akshay Krishnamurthy, John Langford
NeurIPS 2022 Understanding the Eluder Dimension Gene Li, Pritish Kamath, Dylan J Foster, Nati Srebro
NeurIPS 2021 Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination Dylan J Foster, Akshay Krishnamurthy
NeurIPS 2020 Adapting to Misspecification in Contextual Bandits Dylan J Foster, Claudio Gentile, Mehryar Mohri, Julian Zimmert
NeurIPS 2020 Independent Policy Gradient Methods for Competitive Reinforcement Learning Constantinos Daskalakis, Dylan J Foster, Noah Golowich
NeurIPS 2020 Learning the Linear Quadratic Regulator from Nonlinear Observations Zakaria Mhammedi, Dylan J Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford
COLT 2020 Open Problem: Model Selection for Contextual Bandits Dylan J. Foster, Akshay Krishnamurthy, Haipeng Luo
COLT 2020 Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Ayush Sekhari, Karthik Sridharan
IJCAI 2020 Statistical Learning with a Nuisance Component (Extended Abstract) Dylan J. Foster, Vasilis Syrgkanis
NeurIPS 2019 Hypothesis Set Stability and Generalization Dylan J Foster, Spencer Greenberg, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan
NeurIPS 2019 Model Selection for Contextual Bandits Dylan J Foster, Akshay Krishnamurthy, Haipeng Luo
COLT 2019 Statistical Learning with a Nuisance Component Dylan J. Foster, Vasilis Syrgkanis
COLT 2019 Sum-of-Squares Meets Square Loss: Fast Rates for Agnostic Tensor Completion Dylan J. Foster, Andrej Risteski
COLT 2019 The Complexity of Making the Gradient Small in Stochastic Convex Optimization Dylan J. Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan, Blake Woodworth
NeurIPS 2018 Contextual Bandits with Surrogate Losses: Margin Bounds and Efficient Algorithms Dylan J Foster, Akshay Krishnamurthy
AISTATS 2018 Inference in Sparse Graphs with Pairwise Measurements and Side Information Dylan J. Foster, Karthik Sridharan, Daniel Reichman
COLT 2018 Logistic Regression: The Importance of Being Improper Dylan J. Foster, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan
COLT 2018 Online Learning: Sufficient Statistics and the Burkholder Method Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan
NeurIPS 2018 Uniform Convergence of Gradients for Non-Convex Learning and Optimization Dylan J Foster, Ayush Sekhari, Karthik Sridharan
NeurIPS 2017 Parameter-Free Online Learning via Model Selection Dylan J Foster, Satyen Kale, Mehryar Mohri, Karthik Sridharan
NeurIPS 2017 Spectrally-Normalized Margin Bounds for Neural Networks Peter L Bartlett, Dylan J Foster, Matus J Telgarsky
COLT 2017 ZigZag: A New Approach to Adaptive Online Learning Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan
NeurIPS 2016 Learning in Games: Robustness of Fast Convergence Dylan J Foster, Zhiyuan Li, Thodoris Lykouris, Karthik Sridharan, Eva Tardos
NeurIPS 2015 Adaptive Online Learning Dylan J Foster, Alexander Rakhlin, Karthik Sridharan