Privacy-Aware Edge Computing Based on Adaptive DNN Partitioning. - Chengshuai Shi; Lixing Chen; Cong Shen 001; Linqi Song; Jie Xu 001 (GLOBECOM, 2019)

Decentralized Multi-player Multi-armed Bandits with No Collision Information. - Chengshuai Shi; Wei Xiong 015; Cong Shen 001; Jing Yang 002 (AISTATS, 2020)

Federated Multi-Armed Bandits. - Chengshuai Shi; Cong Shen 001 (AAAI, 2021)

Federated Multi-armed Bandits with Personalization. - Chengshuai Shi; Cong Shen 001; Jing Yang 002 (AISTATS, 2021)

An Attackability Perspective on No-Sensing Adversarial Multi-player Multi-armed Bandits. - Chengshuai Shi; Cong Shen 001 (ISIT, 2021)

Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization. - Chengshuai Shi; Wei Xiong 015; Cong Shen 001; Jing Yang 002 (NeurIPS, 2021)

(Almost) Free Incentivized Exploration from Decentralized Learning Agents. - Chengshuai Shi; Haifeng Xu; Wei Xiong 015; Cong Shen 001 (NeurIPS, 2021)

On No-Sensing Adversarial Multi-Player Multi-Armed Bandits With Collision Communications. - Chengshuai Shi; Cong Shen 001 (IEEE J. Sel. Areas Inf. Theory, 2021)

Multi-Player Multi-Armed Bandits With Collision-Dependent Reward Distributions. - Chengshuai Shi; Cong Shen 001 (IEEE Trans. Signal Process., 2021)

A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games. - Wei Xiong 015; Han Zhong 001; Chengshuai Shi; Cong Shen 001; Tong Zhang 001 (ICML, 2022)

Teaching Reinforcement Learning Agents via Reinforcement Learning. - Kun Yang 011; Chengshuai Shi; Cong Shen 001 (CISS, 2023)

Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game. - Wei Xiong 015; Han Zhong 001; Chengshuai Shi; Cong Shen 001; Liwei Wang 001; Tong Zhang 001 (ICLR, 2023)

Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources. - Chengshuai Shi; Wei Xiong 015; Cong Shen 001; Jing Yang 002 (ICML, 2023)

On High-dimensional and Low-rank Tensor Bandits. - Chengshuai Shi; Cong Shen 001; Nicholas D. Sidiropoulos (ISIT, 2023)

Reward Teaching for Federated Multi-armed Bandits. - Chengshuai Shi; Wei Xiong 015; Cong Shen 001; Jing Yang 002 (ISIT, 2023)

Reward Teaching for Federated Multiarmed Bandits. - Chengshuai Shi; Wei Xiong 015; Cong Shen 001; Jing Yang 002 (IEEE Trans. Signal Process., 2023)

Efficient Prompt Optimization Through the Lens of Best Arm Identification. - Chengshuai Shi; Kun Yang 011; Zihan Chen 002; Jundong Li; Jing Yang 002; Cong Shen 001 (NeurIPS, 2024)

Transformers as Game Players: Provable In-context Game-playing Capabilities of Pre-trained Models. - Chengshuai Shi; Kun Yang 011; Jing Yang 002; Cong Shen 001 (NeurIPS, 2024)

Mixture of Demonstrations for In-Context Learning. - Song Wang 013; Zihan Chen 002; Chengshuai Shi; Cong Shen 001; Jundong Li (NeurIPS, 2024)

Best Arm Identification for Prompt Learning under a Limited Budget. - Chengshuai Shi; Kun Yang 011; Jing Yang 002; Cong Shen 001 (CoRR, 2024)

Harnessing the Power of Federated Learning in Federated Contextual Bandits. - Chengshuai Shi; Ruida Zhou; Kun Yang 011; Cong Shen 001 (Trans. Mach. Learn. Res., 2024)

Offline Reinforcement Learning for Wireless Network Optimization With Mixture Datasets. - Kun Yang 011; Chengshuai Shi; Cong Shen 001; Jing Yang 002; Shu-Ping Yeh; Jaroslaw J. Sydir (IEEE Trans. Wirel. Commun., 2024)

Cost-Aware Optimal Pairwise Pure Exploration. - Di Wu; Chengshuai Shi; Ruida Zhou; Cong Shen 001 (AISTATS, 2025)

Building Math Agents with Multi-Turn Iterative Preference Learning. - Wei Xiong 015; Chengshuai Shi; Jiaming Shen; Aviv Rosenberg 002; Zhen Qin 001; Daniele Calandriello; Misha Khalman; Rishabh Joshi; Bilal Piot; Mohammad Saleh; Chi Jin; Tong Zhang 001; et al. (ICLR, 2025)

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis. - Ruiquan Huang; Donghao Li; Chengshuai Shi; Cong Shen 001; Jing Yang 002 (UAI, 2025)