Adaptive Online Learning. - Dylan J. Foster; Alexander Rakhlin; Karthik Sridharan (NIPS, 2015)

Learning in Games: Robustness of Fast Convergence. - Dylan J. Foster; Zhiyuan Li 005; Thodoris Lykouris; Karthik Sridharan; Éva Tardos (NIPS, 2016)

Fast Convergence of Common Learning Algorithms in Games. - Dylan J. Foster; Zhiyuan Li 005; Thodoris Lykouris; Karthik Sridharan; Éva Tardos (CoRR, 2016)

ZigZag: A New Approach to Adaptive Online Learning. - Dylan J. Foster; Alexander Rakhlin; Karthik Sridharan (COLT, 2017)

Spectrally-normalized margin bounds for neural networks. - Peter L. Bartlett; Dylan J. Foster; Matus Telgarsky (NIPS, 2017)

Parameter-Free Online Learning via Model Selection. - Dylan J. Foster; Satyen Kale; Mehryar Mohri; Karthik Sridharan (NIPS, 2017)

Inference in Sparse Graphs with Pairwise Measurements and Side Information. - Dylan J. Foster; Karthik Sridharan; Daniel Reichman 001 (AISTATS, 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 (COLT, 2018)

Practical Contextual Bandits with Regression Oracles. - Dylan J. Foster; Alekh Agarwal; Miroslav Dudík; Haipeng Luo; Robert E. Schapire (ICML, 2018)

Contextual bandits with surrogate losses: Margin bounds and efficient algorithms. - Dylan J. Foster; Akshay Krishnamurthy (NeurIPS, 2018)

Uniform Convergence of Gradients for Non-Convex Learning and Optimization. - Dylan J. Foster; Ayush Sekhari; Karthik Sridharan (NeurIPS, 2018)

Sum-of-squares meets square loss: Fast rates for agnostic tensor completion. - Dylan J. Foster; Andrej Risteski (COLT, 2019)

Statistical Learning with a Nuisance Component. - Dylan J. Foster; Vasilis Syrgkanis (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 E. Woodworth (COLT, 2019)

Distributed Learning with Sublinear Communication. - Jayadev Acharya; Chris De Sa; Dylan J. Foster; Karthik Sridharan (ICML, 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 (NeurIPS, 2019)

Orthogonal Statistical Learning. - Dylan J. Foster; Vasilis Syrgkanis (CoRR, 2019)

𝓁∞ Vector Contraction for Rademacher Complexity. - Dylan J. Foster; Alexander Rakhlin (CoRR, 2019)

Adaptive Learning: Algorithms and Complexity. - Dylan J. Foster (2019)

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 (COLT, 2020)

Open Problem: Model Selection for Contextual Bandits. - Dylan J. Foster; Akshay Krishnamurthy; Haipeng Luo (COLT, 2020)

Tight Bounds on Minimax Regret under Logarithmic Loss via Self-Concordance. - Blair L. Bilodeau; Dylan J. Foster; Daniel M. Roy 001 (ICML, 2020)

Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles. - Dylan J. Foster; Alexander Rakhlin (ICML, 2020)

Logarithmic Regret for Adversarial Online Control. - Dylan J. Foster; Max Simchowitz (ICML, 2020)

Naive Exploration is Optimal for Online LQR. - Max Simchowitz; Dylan J. Foster (ICML, 2020)

Statistical Learning with a Nuisance Component (Extended Abstract). - Dylan J. Foster; Vasilis Syrgkanis (IJCAI, 2020)

Learning nonlinear dynamical systems from a single trajectory. - Dylan J. Foster; Tuhin Sarkar; Alexander Rakhlin (L4DC, 2020)

Independent Policy Gradient Methods for Competitive Reinforcement Learning. - Constantinos Daskalakis; Dylan J. Foster; Noah Golowich (NeurIPS, 2020)

Adapting to Misspecification in Contextual Bandits. - Dylan J. Foster; Claudio Gentile; Mehryar Mohri; Julian Zimmert (NeurIPS, 2020)

Learning the Linear Quadratic Regulator from Nonlinear Observations. - Zakaria Mhammedi; Dylan J. Foster; Max Simchowitz; Dipendra Misra; Wen Sun 002; Akshay Krishnamurthy; Alexander Rakhlin; John Langford 001 (NeurIPS, 2020)

Improved Bounds on Minimax Regret under Logarithmic Loss via Self-Concordance. - Blair L. Bilodeau; Dylan J. Foster; Daniel M. Roy 001 (CoRR, 2020)

Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective. - Dylan J. Foster; Alexander Rakhlin; David Simchi-Levi; Yunzong Xu (COLT, 2021)

Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination. - Dylan J. Foster; Akshay Krishnamurthy (NeurIPS, 2021)

Eluder Dimension and Generalized Rank. - Gene Li; Pritish Kamath; Dylan J. Foster; Nathan Srebro (CoRR, 2021)

The Statistical Complexity of Interactive Decision Making. - Dylan J. Foster; Sham M. Kakade; Jian Qian; Alexander Rakhlin (CoRR, 2021)

Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information. - Yonathan Efroni; Dylan J. Foster; Dipendra Misra; Akshay Krishnamurthy; John Langford 001 (COLT, 2022)

Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation. - Dylan J. Foster; Akshay Krishnamurthy; David Simchi-Levi; Yunzong Xu (COLT, 2022)

Contextual Bandits with Large Action Spaces: Made Practical. - Yinglun Zhu; Dylan J. Foster; John Langford 001; Paul Mineiro (ICML, 2022)

On the Complexity of Adversarial Decision Making. - Dylan J. Foster; Alexander Rakhlin; Ayush Sekhari; Karthik Sridharan (NeurIPS, 2022)

Understanding the Eluder Dimension. - Gene Li; Pritish Kamath; Dylan J. Foster; Nati Srebro (NeurIPS, 2022)

Interaction-Grounded Learning with Action-Inclusive Feedback. - Tengyang Xie; Akanksha Saran; Dylan J. Foster; Lekan P. Molu; Ida Momennejad; Nan Jiang 008; Paul Mineiro; John Langford 001 (NeurIPS, 2022)

Guaranteed Discovery of Controllable Latent States with Multi-Step Inverse Models. - Alex Lamb; Riashat Islam; Yonathan Efroni; Aniket Didolkar; Dipendra Misra; Dylan J. Foster; Lekan P. Molu; Rajan Chari; Akshay Krishnamurthy; John Langford 001 (CoRR, 2022)

Efficient Contextual Bandits with Knapsacks via Regression. - Aleksandrs Slivkins; Dylan J. Foster (CoRR, 2022)

A Note on Model-Free Reinforcement Learning with the Decision-Estimation Coefficient. - Dylan J. Foster; Noah Golowich; Jian Qian; Alexander Rakhlin; Ayush Sekhari (CoRR, 2022)

On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring. - Dean P. Foster; Dylan J. Foster; Noah Golowich; Alexander Rakhlin (COLT, 2023)

Tight Guarantees for Interactive Decision Making with the Decision-Estimation Coefficient. - Dylan J. Foster; Noah Golowich; Yanjun Han (COLT, 2023)

Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regression. - Aleksandrs Slivkins; Karthik Abinav Sankararaman; Dylan J. Foster (COLT, 2023)

Instance-Optimality in Interactive Decision Making: Toward a Non-Asymptotic Theory. - Andrew J. Wagenmaker; Dylan J. Foster (COLT, 2023)

The Role of Coverage in Online Reinforcement Learning. - Tengyang Xie; Dylan J. Foster; Yu Bai 017; Nan Jiang 008; Sham M. Kakade (ICLR, 2023)

Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games. - Dylan J. Foster; Noah Golowich; Sham M. Kakade (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 (ICML, 2023)

Model-Free Reinforcement Learning with the Decision-Estimation Coefficient. - Dylan J. Foster; Noah Golowich; Jian Qian; Alexander Rakhlin; Ayush Sekhari (NeurIPS, 2023)

Efficient Model-Free Exploration in Low-Rank MDPs. - Zakaria Mhammedi; Adam Block; Dylan J. Foster; Alexander Rakhlin (NeurIPS, 2023)

Foundations of Reinforcement Learning and Interactive Decision Making. - Dylan J. Foster; Alexander Rakhlin (CoRR, 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 001 (Trans. Mach. Learn. Res., 2023)

Lower bounds for non-convex stochastic optimization. - Yossi Arjevani; Yair Carmon; John C. Duchi; Dylan J. Foster; Nathan Srebro; Blake E. Woodworth (Math. Program., 2023)

Harnessing Density Ratios for Online Reinforcement Learning. - Philip Amortila; Dylan J. Foster; Nan Jiang 008; Ayush Sekhari; Tengyang Xie (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 (ICLR, 2024)

Rich-Observation Reinforcement Learning with Continuous Latent Dynamics. - Yuda Song 001; Lili Wu; Dylan J. Foster; Akshay Krishnamurthy (ICML, 2024)

Scalable Online Exploration via Coverability. - Philip Amortila; Dylan J. Foster; Akshay Krishnamurthy (ICML, 2024)

Reinforcement Learning Under Latent Dynamics: Toward Statistical and Algorithmic Modularity. - Philip Amortila; Dylan J. Foster; Nan Jiang 008; Akshay Krishnamurthy; Zakaria Mhammedi (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 (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)

Can large language models explore in-context? - Akshay Krishnamurthy; Keegan Harris; Dylan J. Foster; Cyril Zhang; Aleksandrs Slivkins (NeurIPS, 2024)

The Power of Resets in Online Reinforcement Learning. - Zakaria Mhammedi; Dylan J. Foster; Alexander Rakhlin (NeurIPS, 2024)

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)

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 (COLT, 2025)

Necessary and Sufficient Oracles: Toward a Computational Taxonomy for Reinforcement Learning. - Dhruv Rohatgi; Dylan J. Foster (COLT, 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 (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 002; 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 (ICLR, 2025)

Computational-Statistical Tradeoffs at the Next-Token Prediction Barrier: Autoregressive and Imitation Learning under Misspecification. - Dhruv Rohatgi; Adam Block; Audrey Huang; Akshay Krishnamurthy; Dylan J. Foster (CoRR, 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 008; Dylan J. Foster; Akshay Krishnamurthy (CoRR, 2025)