43 citations · 231 across the 26 of their papers we have counts for
27 papers
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…
High-probability sample complexities for policy evaluation with linear function approximation
Gen Li, Weichen Wu, Yuejie Chi +3
This paper is concerned with the problem of policy evaluation with linear function approximation in discounted infinite horizon Markov decision processes. We investigate the sample…
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…
Approximate message passing from random initialization with applications to synchronization
Gen Li, Wei Fan, Yuting Wei
This paper is concerned with the problem of reconstructing an unknown rank-one matrix with prior structural information from noisy observations. While computing the Bayes-optimal e…
Minimax-Optimal Multi-Agent RL in Markov Games With a Generative Model
Gen Li, Yuejie Chi, Yuting Wei +1
This paper studies multi-agent reinforcement learning in Markov games, with the goal of learning Nash equilibria or coarse correlated equilibria (CCE) sample-optimally. All prior r…
A Non-Asymptotic Framework for Approximate Message Passing in Spiked Models
Gen Li, Yuting Wei
Approximate message passing (AMP) emerges as an effective iterative paradigm for solving high-dimensional statistical problems. However, prior AMP theory -- which focused mostly on…