256 citations · 627 across the 29 of their papers we have counts for
36 papers · 1 filter
Gap-Dependent Bounds for Two-Player Markov Games
Zehao Dou, Zhuoran Yang, Zhaoran Wang +1
As one of the most popular methods in the field of reinforcement learning, Q-learning has received increasing attention. Recently, there have been more theoretical works on the reg…
Corruption Robust Active Learning
Yifang Chen, Simon S. Du, Kevin Jamieson
We conduct theoretical studies on streaming-based active learning for binary classification under unknown adversarial label corruptions. In this setting, every time before the lear…
On the Power of Multitask Representation Learning in Linear MDP
Rui Lu, Gao Huang, Simon S. Du
While multitask representation learning has become a popular approach in reinforcement learning (RL), theoretical understanding of why and when it works remains limited. This paper…
Provable Adaptation across Multiway Domains via Representation Learning
Zhili Feng, Shaobo Han, Simon S. Du
This paper studies zero-shot domain adaptation where each domain is indexed on a multi-dimensional array, and we only have data from a small subset of domains. Our goal is to produ…
Improved Corruption Robust Algorithms for Episodic Reinforcement Learning
Yifang Chen, Simon S. Du, Kevin Jamieson
We study episodic reinforcement learning under unknown adversarial corruptions in both the rewards and the transition probabilities of the underlying system. We propose new algorit…
Bilinear Classes: A Structural Framework for Provable Generalization in RL
Simon S. Du, Sham M. Kakade, Jason D. Lee +4
This work introduces Bilinear Classes, a new structural framework, which permit generalization in reinforcement learning in a wide variety of settings through the use of function a…