212 citations · 289 across the 5 of their papers we have counts for
9 papers
Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation
Devavrat Shah, Dogyoon Song, Zhi Xu +1
We consider the question of learning -function in a sample efficient manner for reinforcement learning with continuous state and action spaces under a generative model. If -f…
Stable Reinforcement Learning with Unbounded State Space
Devavrat Shah, Qiaomin Xie, Zhi Xu
We consider the problem of reinforcement learning (RL) with unbounded state space motivated by the classical problem of scheduling in a queueing network. Traditional policies as we…
Rethinking the Value of Labels for Improving Class-Imbalanced Learning
Yuzhe Yang, Zhi Xu
Real-world data often exhibits long-tailed distributions with heavy class imbalance, posing great challenges for deep recognition models. We identify a persisting dilemma on the va…
On Reinforcement Learning for Turn-based Zero-sum Markov Games
Devavrat Shah, Varun Somani, Qiaomin Xie +1
We consider the problem of finding Nash equilibrium for two-player turn-based zero-sum games. Inspired by the AlphaGo Zero (AGZ) algorithm, we develop a Reinforcement Learning base…
Harnessing Structures for Value-Based Planning and Reinforcement Learning
Yuzhe Yang, Guo Zhang, Zhi Xu +1
Value-based methods constitute a fundamental methodology in planning and deep reinforcement learning (RL). In this paper, we propose to exploit the underlying structures of the sta…
ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation
Yuzhe Yang, Guo Zhang, Dina Katabi +1
Deep neural networks are vulnerable to adversarial attacks. The literature is rich with algorithms that can easily craft successful adversarial examples. In contrast, the performan…