174 citations · 680 across the 52 of their papers we have counts for
7 papers · 1 filter
The Statistical Complexity of Interactive Decision Making
Dylan J. Foster, Sham M. Kakade, Jian Qian +1
A fundamental challenge in interactive learning and decision making, ranging from bandit problems to reinforcement learning, is to provide sample-efficient, adaptive learning algor…
Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation
Dylan J. Foster, Akshay Krishnamurthy, David Simchi-Levi +1
We consider the offline reinforcement learning problem, where the aim is to learn a decision making policy from logged data. Offline RL -- particularly when coupled with (value) fu…
Minimax Rates for Conditional Density Estimation via Empirical Entropy
Blair Bilodeau, Dylan J. Foster, Daniel M. Roy
We consider the task of estimating a conditional density using i.i.d. samples from a joint distribution, which is a fundamental problem with applications in both classification and…
Adapting to Misspecification in Contextual Bandits
Dylan J. Foster, Claudio Gentile, Mehryar Mohri +1
A major research direction in contextual bandits is to develop algorithms that are computationally efficient, yet support flexible, general-purpose function approximation. Algorith…
Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination
Dylan J. Foster, Akshay Krishnamurthy
A recurring theme in statistical learning, online learning, and beyond is that faster convergence rates are possible for problems with low noise, often quantified by the performanc…
Understanding the Eluder Dimension
Gene Li, Pritish Kamath, Dylan J. Foster +1
We provide new insights on eluder dimension, a complexity measure that has been extensively used to bound the regret of algorithms for online bandits and reinforcement learning wit…
Independent Policy Gradient Methods for Competitive Reinforcement Learning
Constantinos Daskalakis, Dylan J. Foster, Noah Golowich
We obtain global, non-asymptotic convergence guarantees for independent learning algorithms in competitive reinforcement learning settings with two agents (i.e., zero-sum stochasti…