9 citations · 15 across the 6 of their papers we have counts for
4 papers · 1 filter
Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selection
Yu Bai, Fan Chen, Huan Wang +2
Neural sequence models based on the transformer architecture have demonstrated remarkable \emph{in-context learning} (ICL) abilities, where they can perform new tasks when prompted…
Lower Bounds for Learning in Revealing POMDPs
Fan Chen, Huan Wang, Caiming Xiong +2
This paper studies the fundamental limits of reinforcement learning (RL) in the challenging \emph{partially observable} setting. While it is well-established that learning in Parti…
Partially Observable RL with B-Stability: Unified Structural Condition and Sharp Sample-Efficient Algorithms
Fan Chen, Yu Bai, Song Mei
Partial Observability -- where agents can only observe partial information about the true underlying state of the system -- is ubiquitous in real-world applications of Reinforcemen…
A Near-Optimal Primal-Dual Method for Off-Policy Learning in CMDP
Fan Chen, Junyu Zhang, Zaiwen Wen
As an important framework for safe Reinforcement Learning, the Constrained Markov Decision Process (CMDP) has been extensively studied in the recent literature. However, despite th…