174 citations · 313 across the 14 of their papers we have counts for
24 papers
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…
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…
Learning the Linear Quadratic Regulator from Nonlinear Observations
Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz +5
We introduce a new problem setting for continuous control called the LQR with Rich Observations, or RichLQR. In our setting, the environment is summarized by a low-dimensional cont…
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective
Dylan J. Foster, Alexander Rakhlin, David Simchi-Levi +1
In the classical multi-armed bandit problem, instance-dependent algorithms attain improved performance on "easy" problems with a gap between the best and second-best arm. Are simil…
Tight Bounds on Minimax Regret under Logarithmic Loss via Self-Concordance
Blair Bilodeau, Dylan J. Foster, Daniel M. Roy
We consider the classical problem of sequential probability assignment under logarithmic loss while competing against an arbitrary, potentially nonparametric class of experts. We o…