15 citations · 52 across the 10 of their papers we have counts for
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stat.ML2023
Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources
Chengshuai Shi, Wei Xiong, Cong Shen +1
Existing theoretical studies on offline reinforcement learning (RL) mostly consider a dataset sampled directly from the target task. In practice, however, data often come from seve…
stat.ML2021★ 6 cited
Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization
Chengshuai Shi, Wei Xiong, Cong Shen +1
Despite the significant interests and many progresses in decentralized multi-player multi-armed bandits (MP-MAB) problems in recent years, the regret gap to the natural centralized…
stat.ML2021★ 15 cited
Federated Linear Contextual Bandits
Ruiquan Huang, Weiqiang Wu, Jing Yang +1
This paper presents a novel federated linear contextual bandits model, where individual clients face different -armed stochastic bandits coupled through common global parameters…