Dylan J. Foster

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 108

Top Venues

CoRR

COLT

NeurIPS

ICML

Publications

Adam Block, Dylan J. Foster, Akshay Krishnamurthy, Max Simchowitz, Cyril Zhang
Butterfly Effects of SGD Noise: Error Amplification in Behavior Cloning and Autoregression. ICLR (2024)
Akshay Krishnamurthy, Keegan Harris, Dylan J. Foster, Cyril Zhang, Aleksandrs Slivkins
Can large language models explore in-context? CoRR (2024)
Tengyang Xie, Dylan J. Foster, Akshay Krishnamurthy, Corby Rosset, Ahmed Awadallah, Alexander Rakhlin
Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF. CoRR (2024)
Yuda Song, Lili Wu, Dylan J. Foster, Akshay Krishnamurthy
Rich-Observation Reinforcement Learning with Continuous Latent Dynamics. CoRR (2024)
Philip Amortila, Dylan J. Foster, Nan Jiang, Ayush Sekhari, Tengyang Xie
Harnessing Density Ratios for Online Reinforcement Learning. CoRR (2024)
Dylan J. Foster, Yanjun Han, Jian Qian, Alexander Rakhlin
Online Estimation via Offline Estimation: An Information-Theoretic Framework. CoRR (2024)
Philip Amortila, Dylan J. Foster, Akshay Krishnamurthy
Scalable Online Exploration via Coverability. CoRR (2024)
Audrey Huang, Wenhao Zhan, Tengyang Xie, Jason D. Lee, Wen Sun, Akshay Krishnamurthy, Dylan J. Foster
Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization. CoRR (2024)
Zakaria Mhammedi, Dylan J. Foster, Alexander Rakhlin
The Power of Resets in Online Reinforcement Learning. CoRR (2024)
Philip Amortila, Dylan J. Foster, Nan Jiang, Ayush Sekhari, Tengyang Xie
Harnessing Density Ratios for Online Reinforcement Learning. ICLR (2024)
Dylan J. Foster, Adam Block, Dipendra Misra
Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning. CoRR (2024)
Adam Block, Dylan J. Foster, Akshay Krishnamurthy, Max Simchowitz, Cyril Zhang
Butterfly Effects of SGD Noise: Error Amplification in Behavior Cloning and Autoregression. CoRR (2023)
Zakaria Mhammedi, Dylan J. Foster, Alexander Rakhlin
Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL. ICML (2023)
Dylan J. Foster, Noah Golowich, Sham M. Kakade
Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games. ICML (2023)
Andrew Wagenmaker, Dylan J. Foster
Instance-Optimality in Interactive Decision Making: Toward a Non-Asymptotic Theory. CoRR (2023)
Dylan J. Foster, Noah Golowich, Yanjun Han
Tight Guarantees for Interactive Decision Making with the Decision-Estimation Coefficient. COLT (2023)
Dylan J. Foster, Noah Golowich, Sham M. Kakade
Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games. CoRR (2023)
Zakaria Mhammedi, Dylan J. Foster, Alexander Rakhlin
Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL. CoRR (2023)
Dylan J. Foster, Dean P. Foster, Noah Golowich, Alexander Rakhlin
On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring. CoRR (2023)
Andrew J. Wagenmaker, Dylan J. Foster
Instance-Optimality in Interactive Decision Making: Toward a Non-Asymptotic Theory. COLT (2023)
Dylan J. Foster, Noah Golowich, Yanjun Han
Tight Guarantees for Interactive Decision Making with the Decision-Estimation Coefficient. CoRR (2023)
Zakaria Mhammedi, Adam Block, Dylan J. Foster, Alexander Rakhlin
Efficient Model-Free Exploration in Low-Rank MDPs. NeurIPS (2023)
Dylan J. Foster, Alexander Rakhlin
Foundations of Reinforcement Learning and Interactive Decision Making. CoRR (2023)
Zakaria Mhammedi, Adam Block, Dylan J. Foster, Alexander Rakhlin
Efficient Model-Free Exploration in Low-Rank MDPs. CoRR (2023)
Aleksandrs Slivkins, Karthik Abinav Sankararaman, Dylan J. Foster
Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regression. COLT (2023)
Alex Lamb, Riashat Islam, Yonathan Efroni, Aniket Rajiv Didolkar, Dipendra Misra, Dylan J. Foster, Lekan P. Molu, Rajan Chari, Akshay Krishnamurthy, John Langford
Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models. Trans. Mach. Learn. Res. 2023 (2023)
Dylan J. Foster, Noah Golowich, Jian Qian, Alexander Rakhlin, Ayush Sekhari
Model-Free Reinforcement Learning with the Decision-Estimation Coefficient. NeurIPS (2023)
Dean Foster, Dylan J. Foster, Noah Golowich, Alexander Rakhlin
On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring. COLT (2023)
Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Nathan Srebro, Blake E. Woodworth
Lower bounds for non-convex stochastic optimization. Math. Program. 199 (1) (2023)
Tengyang Xie, Dylan J. Foster, Yu Bai, Nan Jiang, Sham M. Kakade
The Role of Coverage in Online Reinforcement Learning. ICLR (2023)
Dylan J. Foster, Noah Golowich, Jian Qian, Alexander Rakhlin, Ayush Sekhari
A Note on Model-Free Reinforcement Learning with the Decision-Estimation Coefficient. CoRR (2022)
Yonathan Efroni, Dylan J. Foster, Dipendra Misra, Akshay Krishnamurthy, John Langford
Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information. CoRR (2022)
Alex Lamb, Riashat Islam, Yonathan Efroni, Aniket Didolkar, Dipendra Misra, Dylan J. Foster, Lekan Molu, Rajan Chari, Akshay Krishnamurthy, John Langford
Guaranteed Discovery of Controllable Latent States with Multi-Step Inverse Models. CoRR (2022)
Dylan J. Foster, Alexander Rakhlin, Ayush Sekhari, Karthik Sridharan
On the Complexity of Adversarial Decision Making. CoRR (2022)
Tengyang Xie, Dylan J. Foster, Yu Bai, Nan Jiang, Sham M. Kakade
The Role of Coverage in Online Reinforcement Learning. CoRR (2022)
Tengyang Xie, Akanksha Saran, Dylan J. Foster, Lekan P. Molu, Ida Momennejad, Nan Jiang, Paul Mineiro, John Langford
Interaction-Grounded Learning with Action-Inclusive Feedback. NeurIPS (2022)
Dylan J. Foster, Alexander Rakhlin, Ayush Sekhari, Karthik Sridharan
On the Complexity of Adversarial Decision Making. NeurIPS (2022)
Aleksandrs Slivkins, Dylan J. Foster
Efficient Contextual Bandits with Knapsacks via Regression. CoRR (2022)
Yinglun Zhu, Dylan J. Foster, John Langford, Paul Mineiro
Contextual Bandits with Large Action Spaces: Made Practical. CoRR (2022)
Dylan J. Foster, Akshay Krishnamurthy, David Simchi-Levi, Yunzong Xu
Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation. COLT (2022)
Tengyang Xie, Akanksha Saran, Dylan J. Foster, Lekan Molu, Ida Momennejad, Nan Jiang, Paul Mineiro, John Langford
Interaction-Grounded Learning with Action-inclusive Feedback. CoRR (2022)
Gene Li, Pritish Kamath, Dylan J. Foster, Nati Srebro
Understanding the Eluder Dimension. NeurIPS (2022)
Yinglun Zhu, Dylan J. Foster, John Langford, Paul Mineiro
Contextual Bandits with Large Action Spaces: Made Practical. ICML (2022)
Yonathan Efroni, Dylan J. Foster, Dipendra Misra, Akshay Krishnamurthy, John Langford
Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information. COLT (2022)
Dylan J. Foster, Akshay Krishnamurthy
Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination. NeurIPS (2021)
Dylan J. Foster, Sham M. Kakade, Jian Qian, Alexander Rakhlin
The Statistical Complexity of Interactive Decision Making. CoRR (2021)
Dylan J. Foster, Akshay Krishnamurthy
Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination. CoRR (2021)
Dylan J. Foster, Claudio Gentile, Mehryar Mohri, Julian Zimmert
Adapting to Misspecification in Contextual Bandits. CoRR (2021)
Dylan J. Foster, Alexander Rakhlin, David Simchi-Levi, Yunzong Xu
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective. COLT (2021)
Gene Li, Pritish Kamath, Dylan J. Foster, Nathan Srebro
Eluder Dimension and Generalized Rank. CoRR (2021)
Dylan J. Foster, Akshay Krishnamurthy, David Simchi-Levi, Yunzong Xu
Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation. CoRR (2021)
Constantinos Daskalakis, Dylan J. Foster, Noah Golowich
Independent Policy Gradient Methods for Competitive Reinforcement Learning. CoRR (2021)
Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford
Learning the Linear Quadratic Regulator from Nonlinear Observations. CoRR (2020)
Max Simchowitz, Dylan J. Foster
Naive Exploration is Optimal for Online LQR. CoRR (2020)
Dylan J. Foster, Alexander Rakhlin, David Simchi-Levi, Yunzong Xu
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective. CoRR (2020)
Dylan J. Foster, Max Simchowitz
Logarithmic Regret for Adversarial Online Control. ICML (2020)
Dylan J. Foster, Max Simchowitz
Logarithmic Regret for Adversarial Online Control. CoRR (2020)
Blair Bilodeau, Dylan J. Foster, Daniel M. Roy
Improved Bounds on Minimax Regret under Logarithmic Loss via Self-Concordance. CoRR (2020)
Dylan J. Foster, Alexander Rakhlin
Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles. ICML (2020)
Constantinos Daskalakis, Dylan J. Foster, Noah Golowich
Independent Policy Gradient Methods for Competitive Reinforcement Learning. NeurIPS (2020)
Dylan J. Foster, Akshay Krishnamurthy, Haipeng Luo
Open Problem: Model Selection for Contextual Bandits. CoRR (2020)
Dylan J. Foster, Claudio Gentile, Mehryar Mohri, Julian Zimmert
Adapting to Misspecification in Contextual Bandits. NeurIPS (2020)
Dylan J. Foster, Alexander Rakhlin, Tuhin Sarkar
Learning nonlinear dynamical systems from a single trajectory. CoRR (2020)
Dylan J. Foster, Akshay Krishnamurthy, Haipeng Luo
Open Problem: Model Selection for Contextual Bandits. COLT (2020)
Blair Bilodeau, Dylan J. Foster, Daniel M. Roy
Tight Bounds on Minimax Regret under Logarithmic Loss via Self-Concordance. ICML (2020)
Dylan J. Foster, Tuhin Sarkar, Alexander Rakhlin
Learning nonlinear dynamical systems from a single trajectory. L4DC (2020)
Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford
Learning the Linear Quadratic Regulator from Nonlinear Observations. NeurIPS (2020)
Max Simchowitz, Dylan J. Foster
Naive Exploration is Optimal for Online LQR. ICML (2020)
Dylan J. Foster, Alexander Rakhlin
Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles. CoRR (2020)
Dylan J. Foster, Vasilis Syrgkanis
Statistical Learning with a Nuisance Component (Extended Abstract). IJCAI (2020)
Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Ayush Sekhari, Karthik Sridharan
Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations. COLT (2020)
Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Ayush Sekhari, Karthik Sridharan
Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations. CoRR (2020)
Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Nathan Srebro, Blake E. Woodworth
Lower Bounds for Non-Convex Stochastic Optimization. CoRR (2019)
Dylan J. Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan, Blake E. Woodworth
The Complexity of Making the Gradient Small in Stochastic Convex Optimization. COLT (2019)
Dylan J. Foster, Spencer Greenberg, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan
Hypothesis Set Stability and Generalization. NeurIPS (2019)
Dylan J. Foster, Andrej Risteski
Sum-of-squares meets square loss: Fast rates for agnostic tensor completion. COLT (2019)
Dylan J. Foster, Andrej Risteski
Sum-of-squares meets square loss: Fast rates for agnostic tensor completion. CoRR (2019)
Dylan J. Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan, Blake E. Woodworth
The Complexity of Making the Gradient Small in Stochastic Convex Optimization. CoRR (2019)
Dylan J. Foster, Alexander Rakhlin
Vector Contraction for Rademacher Complexity. CoRR (2019)
Dylan J. Foster, Akshay Krishnamurthy, Haipeng Luo
Model selection for contextual bandits. CoRR (2019)
Dylan J. Foster, Akshay Krishnamurthy, Haipeng Luo
Model Selection for Contextual Bandits. NeurIPS (2019)
Dylan J. Foster, Vasilis Syrgkanis
Statistical Learning with a Nuisance Component. COLT (2019)
Dylan J. Foster, Vasilis Syrgkanis
Orthogonal Statistical Learning. CoRR (2019)
Dylan J. Foster, Spencer Greenberg, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan
Hypothesis Set Stability and Generalization. CoRR (2019)
Jayadev Acharya, Christopher De Sa, Dylan J. Foster, Karthik Sridharan
Distributed Learning with Sublinear Communication. CoRR (2019)
Jayadev Acharya, Chris De Sa, Dylan J. Foster, Karthik Sridharan
Distributed Learning with Sublinear Communication. ICML (2019)
Dylan J. Foster, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan
Logistic Regression: The Importance of Being Improper. COLT (2018)
Dylan J. Foster, Ayush Sekhari, Karthik Sridharan
Uniform Convergence of Gradients for Non-Convex Learning and Optimization. NeurIPS (2018)
Dylan J. Foster, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan
Logistic Regression: The Importance of Being Improper. CoRR (2018)
Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan
Online Learning: Sufficient Statistics and the Burkholder Method. COLT (2018)
Dylan J. Foster, Alekh Agarwal, Miroslav Dudík, Haipeng Luo, Robert E. Schapire
Practical Contextual Bandits with Regression Oracles. ICML (2018)
Dylan J. Foster, Satyen Kale, Mehryar Mohri, Karthik Sridharan
Parameter-free online learning via model selection. CoRR (2018)
Dylan J. Foster, Karthik Sridharan, Daniel Reichman
Inference in Sparse Graphs with Pairwise Measurements and Side Information. AISTATS (2018)
Dylan J. Foster, Ayush Sekhari, Karthik Sridharan
Uniform Convergence of Gradients for Non-Convex Learning and Optimization. CoRR (2018)
Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan
Online Learning: Sufficient Statistics and the Burkholder Method. CoRR (2018)
Dylan J. Foster, Akshay Krishnamurthy
Contextual bandits with surrogate losses: Margin bounds and efficient algorithms. CoRR (2018)
Dylan J. Foster, Alekh Agarwal, Miroslav Dudík, Haipeng Luo, Robert E. Schapire
Practical Contextual Bandits with Regression Oracles. CoRR (2018)
Dylan J. Foster, Akshay Krishnamurthy
Contextual bandits with surrogate losses: Margin bounds and efficient algorithms. NeurIPS (2018)
Peter L. Bartlett, Dylan J. Foster, Matus Telgarsky
Spectrally-normalized margin bounds for neural networks. CoRR (2017)
Dylan J. Foster, Satyen Kale, Mehryar Mohri, Karthik Sridharan
Parameter-Free Online Learning via Model Selection. NIPS (2017)

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Publication Activity (10 Years)

Top Topics

Top Venues

Dylan J. Foster