9 citations · 15 across the 6 of their papers we have counts for
6 papers
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…
Convergence Analysis of Stochastic Gradient Descent with MCMC Estimators
Tianyou Li, Fan Chen, Huajie Chen +1
Understanding stochastic gradient descent (SGD) and its variants is essential for machine learning. However, most of the preceding analyses are conducted under amenable conditions…
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 Unified Primal-Dual Algorithm Framework for Inequality Constrained Problems
Zhenyuan Zhu, Fan Chen, Junyu Zhang +1
In this paper, we propose a unified primal-dual algorithm framework based on the augmented Lagrangian function for composite convex problems with conic inequality constraints. The…
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…