378 citations · 1.8k across the 48 of their papers we have counts for
7 papers · 1 filter
Settling the Sample Complexity of Online Reinforcement Learning
Zihan Zhang, Yuxin Chen, Jason D. Lee +1
A central issue lying at the heart of online reinforcement learning (RL) is data efficiency. While a number of recent works achieved asymptotically minimal regret in online RL, the…
Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models
Gen Li, Yuting Wei, Yuxin Chen +1
Diffusion models, which convert noise into new data instances by learning to reverse a Markov diffusion process, have become a cornerstone in contemporary generative modeling. Whil…
Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning
Gen Li, Wenhao Zhan, Jason D. Lee +2
This paper studies tabular reinforcement learning (RL) in the hybrid setting, which assumes access to both an offline dataset and online interactions with the unknown environment.…
The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model
Laixi Shi, Gen Li, Yuting Wei +3
This paper investigates model robustness in reinforcement learning (RL) to reduce the sim-to-real gap in practice. We adopt the framework of distributionally robust Markov decision…
Minimax-Optimal Reward-Agnostic Exploration in Reinforcement Learning
Gen Li, Yuling Yan, Yuxin Chen +1
This paper studies reward-agnostic exploration in reinforcement learning (RL) -- a scenario where the learner is unware of the reward functions during the exploration stage -- and…
Deflated HeteroPCA: Overcoming the curse of ill-conditioning in heteroskedastic PCA
Yuchen Zhou, Yuxin Chen
This paper is concerned with estimating the column subspace of a low-rank matrix from contaminated data. How to obtain optimal…