Dylan J. Foster

According to our database, Dylan J. Foster authored at least 63 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Can large language models explore in-context?

Akshay Krishnamurthy, Keegan Harris, Dylan J. Foster, Cyril Zhang, Aleksandrs Slivkins

CoRR, 2024

Scalable Online Exploration via Coverability.

Philip Amortila, Dylan J. Foster, Akshay Krishnamurthy

CoRR, 2024

Harnessing Density Ratios for Online Reinforcement Learning.

Philip Amortila, Dylan J. Foster, Nan Jiang, Ayush Sekhari, Tengyang Xie

CoRR, 2024

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., May, 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

Trans. Mach. Learn. Res., 2023

Foundations of Reinforcement Learning and Interactive Decision Making.

Dylan J. Foster, Alexander Rakhlin

CoRR, 2023

Butterfly Effects of SGD Noise: Error Amplification in Behavior Cloning and Autoregression.

Adam Block, Dylan J. Foster, Akshay Krishnamurthy, Max Simchowitz, Cyril Zhang

CoRR, 2023

On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring.

Dylan J. Foster, Dean P. Foster, Noah Golowich, Alexander Rakhlin

CoRR, 2023

Efficient Model-Free Exploration in Low-Rank MDPs.

Zakaria Mhammedi, Adam Block, Dylan J. Foster, Alexander Rakhlin

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Model-Free Reinforcement Learning with the Decision-Estimation Coefficient.

Dylan J. Foster, Noah Golowich, Jian Qian, Alexander Rakhlin, Ayush Sekhari

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL.

Zakaria Mhammedi, Dylan J. Foster, Alexander Rakhlin

Proceedings of the International Conference on Machine Learning, 2023

Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games.

Dylan J. Foster, Noah Golowich, Sham M. Kakade

Proceedings of the International Conference on Machine Learning, 2023

The Role of Coverage in Online Reinforcement Learning.

Tengyang Xie, Dylan J. Foster, Yu Bai, Nan Jiang, Sham M. Kakade

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Instance-Optimality in Interactive Decision Making: Toward a Non-Asymptotic Theory.

Andrew J. Wagenmaker, Dylan J. Foster

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regression.

Aleksandrs Slivkins, Karthik Abinav Sankararaman, Dylan J. Foster

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Tight Guarantees for Interactive Decision Making with the Decision-Estimation Coefficient.

Dylan J. Foster, Noah Golowich, Yanjun Han

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 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

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

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

Efficient Contextual Bandits with Knapsacks via Regression.

Aleksandrs Slivkins, Dylan J. Foster

CoRR, 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

CoRR, 2022

Interaction-Grounded Learning with Action-Inclusive Feedback.

Tengyang Xie, Akanksha Saran, Dylan J. Foster, Lekan P. Molu, Ida Momennejad, Nan Jiang, Paul Mineiro, John Langford

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Understanding the Eluder Dimension.

Gene Li, Pritish Kamath, Dylan J. Foster, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On the Complexity of Adversarial Decision Making.

Dylan J. Foster, Alexander Rakhlin, Ayush Sekhari, Karthik Sridharan

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Contextual Bandits with Large Action Spaces: Made Practical.

Yinglun Zhu, Dylan J. Foster, John Langford, Paul Mineiro

Proceedings of the International Conference on Machine Learning, 2022

Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation.

Dylan J. Foster, Akshay Krishnamurthy, David Simchi-Levi, Yunzong Xu

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information.

Yonathan Efroni, Dylan J. Foster, Dipendra Misra, Akshay Krishnamurthy, John Langford

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021

The Statistical Complexity of Interactive Decision Making.

Dylan J. Foster, Sham M. Kakade, Jian Qian, Alexander Rakhlin

CoRR, 2021

Eluder Dimension and Generalized Rank.

Gene Li, Pritish Kamath, Dylan J. Foster, Nathan Srebro

CoRR, 2021

Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination.

Dylan J. Foster, Akshay Krishnamurthy

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective.

Dylan J. Foster, Alexander Rakhlin, David Simchi-Levi, Yunzong Xu

Proceedings of the Conference on Learning Theory, 2021

2020

Improved Bounds on Minimax Regret under Logarithmic Loss via Self-Concordance.

Blair L. Bilodeau, Dylan J. Foster, Daniel M. Roy

CoRR, 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

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Adapting to Misspecification in Contextual Bandits.

Dylan J. Foster, Claudio Gentile, Mehryar Mohri, Julian Zimmert

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Independent Policy Gradient Methods for Competitive Reinforcement Learning.

Constantinos Daskalakis, Dylan J. Foster, Noah Golowich

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning nonlinear dynamical systems from a single trajectory.

Dylan J. Foster, Tuhin Sarkar, Alexander Rakhlin

Proceedings of the 2nd Annual Conference on Learning for Dynamics and Control, 2020

Statistical Learning with a Nuisance Component (Extended Abstract).

Dylan J. Foster, Vasilis Syrgkanis

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Naive Exploration is Optimal for Online LQR.

Max Simchowitz, Dylan J. Foster

Proceedings of the 37th International Conference on Machine Learning, 2020

Logarithmic Regret for Adversarial Online Control.

Dylan J. Foster, Max Simchowitz

Proceedings of the 37th International Conference on Machine Learning, 2020

Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles.

Dylan J. Foster, Alexander Rakhlin

Proceedings of the 37th International Conference on Machine Learning, 2020

Tight Bounds on Minimax Regret under Logarithmic Loss via Self-Concordance.

Blair L. Bilodeau, Dylan J. Foster, Daniel M. Roy

Proceedings of the 37th International Conference on Machine Learning, 2020

Open Problem: Model Selection for Contextual Bandits.

Dylan J. Foster, Akshay Krishnamurthy, Haipeng Luo

Proceedings of the Conference on Learning Theory, 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

Proceedings of the Conference on Learning Theory, 2020

2019

Adaptive Learning: Algorithms and Complexity.

Dylan J. Foster

PhD thesis, 2019

𝓁_∞ Vector Contraction for Rademacher Complexity.

Dylan J. Foster, Alexander Rakhlin

CoRR, 2019

Orthogonal Statistical Learning.

Dylan J. Foster, Vasilis Syrgkanis

CoRR, 2019

Model Selection for Contextual Bandits.

Dylan J. Foster, Akshay Krishnamurthy, Haipeng Luo

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Hypothesis Set Stability and Generalization.

Dylan J. Foster, Spencer Greenberg, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Distributed Learning with Sublinear Communication.

Jayadev Acharya, Chris De Sa, Dylan J. Foster, Karthik Sridharan

Proceedings of the 36th International Conference on Machine Learning, 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

Proceedings of the Conference on Learning Theory, 2019

Statistical Learning with a Nuisance Component.

Dylan J. Foster, Vasilis Syrgkanis

Proceedings of the Conference on Learning Theory, 2019

Sum-of-squares meets square loss: Fast rates for agnostic tensor completion.

Dylan J. Foster, Andrej Risteski

Proceedings of the Conference on Learning Theory, 2019

2018

Uniform Convergence of Gradients for Non-Convex Learning and Optimization.

Dylan J. Foster, Ayush Sekhari, Karthik Sridharan

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Contextual bandits with surrogate losses: Margin bounds and efficient algorithms.

Dylan J. Foster, Akshay Krishnamurthy

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Practical Contextual Bandits with Regression Oracles.

Dylan J. Foster, Alekh Agarwal, Miroslav Dudík, Haipeng Luo, Robert E. Schapire

Proceedings of the 35th International Conference on Machine Learning, 2018

Online Learning: Sufficient Statistics and the Burkholder Method.

Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan

Proceedings of the Conference On Learning Theory, 2018

Logistic Regression: The Importance of Being Improper.

Dylan J. Foster, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan

Proceedings of the Conference On Learning Theory, 2018

Inference in Sparse Graphs with Pairwise Measurements and Side Information.

Dylan J. Foster, Karthik Sridharan, Daniel Reichman

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Parameter-Free Online Learning via Model Selection.

Dylan J. Foster, Satyen Kale, Mehryar Mohri, Karthik Sridharan

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Spectrally-normalized margin bounds for neural networks.

Peter L. Bartlett, Dylan J. Foster, Matus Telgarsky

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

ZigZag: A New Approach to Adaptive Online Learning.

Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan

Proceedings of the 30th Conference on Learning Theory, 2017

2016

Fast Convergence of Common Learning Algorithms in Games.

Dylan J. Foster, Zhiyuan Li, Thodoris Lykouris, Karthik Sridharan, Éva Tardos

CoRR, 2016

Learning in Games: Robustness of Fast Convergence.

Dylan J. Foster, Zhiyuan Li, Thodoris Lykouris, Karthik Sridharan, Éva Tardos

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

Adaptive Online Learning.

Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015