15 citations · 61 across the 28 of their papers we have counts for
4 papers · 1 filter
Harnessing the Power of Federated Learning in Federated Contextual Bandits
Chengshuai Shi, Ruida Zhou, Kun Yang +1
Federated learning (FL) has demonstrated great potential in revolutionizing distributed machine learning, and tremendous efforts have been made to extend it beyond the original foc…
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…
On High-dimensional and Low-rank Tensor Bandits
Chengshuai Shi, Cong Shen, Nicholas D. Sidiropoulos
Most existing studies on linear bandits focus on the one-dimensional characterization of the overall system. While being representative, this formulation may fail to model applicat…
Reward Teaching for Federated Multi-armed Bandits
Chengshuai Shi, Wei Xiong, Cong Shen +1
Most of the existing federated multi-armed bandits (FMAB) designs are based on the presumption that clients will implement the specified design to collaborate with the server. In r…