174 citations · 680 across the 52 of their papers we have counts for
8 papers · 1 filter
Model-Free Reinforcement Learning with the Decision-Estimation Coefficient
Dylan J. Foster, Noah Golowich, Jian Qian +2
We consider the problem of interactive decision making, encompassing structured bandits and reinforcement learning with general function approximation. Recently, Foster et al. (202…
Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regression
Aleksandrs Slivkins, Xingyu Zhou, Karthik Abinav Sankararaman +1
We consider contextual bandits with linear constraints (CBwLC), a variant of contextual bandits in which the algorithm consumes multiple resources subject to linear constraints on…
The Role of Coverage in Online Reinforcement Learning
Tengyang Xie, Dylan J. Foster, Yu Bai +2
Coverage conditions -- which assert that the data logging distribution adequately covers the state space -- play a fundamental role in determining the sample complexity of offline…
Contextual Bandits with Large Action Spaces: Made Practical
Yinglun Zhu, Dylan J. Foster, John Langford +1
A central problem in sequential decision making is to develop algorithms that are practical and computationally efficient, yet support the use of flexible, general-purpose models.…
Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models
Alex Lamb, Riashat Islam, Yonathan Efroni +7
In many sequential decision-making tasks, the agent is not able to model the full complexity of the world, which consists of multitudes of relevant and irrelevant information. For…
On the Complexity of Adversarial Decision Making
Dylan J. Foster, Alexander Rakhlin, Ayush Sekhari +1
A central problem in online learning and decision making -- from bandits to reinforcement learning -- is to understand what modeling assumptions lead to sample-efficient learning g…
Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information
Yonathan Efroni, Dylan J. Foster, Dipendra Misra +2
In real-world reinforcement learning applications the learner's observation space is ubiquitously high-dimensional with both relevant and irrelevant information about the task at h…
Interaction-Grounded Learning with Action-inclusive Feedback
Tengyang Xie, Akanksha Saran, Dylan J. Foster +5
Consider the problem setting of Interaction-Grounded Learning (IGL), in which a learner's goal is to optimally interact with the environment with no explicit reward to ground its p…