Chengshuai Shi
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Fourth-year Ph.D. student Email: cs7ync at virginia dot edu Phone: 434-218-9860 Advisor: Prof. Cong Shen About this picture: My mother drew this Dr. Cat wearing a graduation cap when I was in high school. Now I am working towards a Ph.D. and hopefully, my family can see me wearing it one day:) Since Fall 2021, I have been supported by the generous Bloomberg Data Science Ph.D. fellowship. |
Research Interest
My current research focuses on multi-armed bandits (MAB) problems and more general reinforcement learning problems, especially their multi-agent versions. I also have broad interests in distributed learning and federated learning.
News
04/2023: Selected to receive the 2023 Charles L. Brown Department of Electrical and Computer Engineering’s Louis T Rader Graduate Research Award!
04/2023: One paper accepted to ICML 2023!
Our paper “Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources” (with Wei Xiong, Prof. Cong Shen and Prof. Jing Yang) is accepted to be presented at ICML 2022.
04/2023: Two papers accepted to IEEE ISIT 2023!
The first one titled “On High-Dimensional and Low-Rank Tensor Bandits” (with Prof. Cong Shen and Prof. Nikolaos Sidiropoulos) is on extending the one-dimensional system in linear bandits to a high-dimensional one characterized by low-rank tensors;
The second one titled “Reward Teaching for Federated Multi-Armed Bandits” (with Wei Xiong, Prof. Cong Shen and Prof. Jing Yang) is on using implicit reward adjustments to guide autonomous bandit agents in a federated multi-armed bandit system.
01/2023: One paper accepted to ICLR 2023!
Our paper “Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game” (with Wei Xiong, Han Zhong, Prof. Cong Shen, Prof. Liwei Wang, and Prof. Tong Zhang) is accepted to be presented at ICLR 2023.
11/2022: Selected to receive the 2022–2023 UVA Engineering Endowed Graduate Fellowship!
09/2022: Finished a three-month internship at Bloomberg, NY!