15 citations · 75 across the 9 of their papers we have counts for
10 papers
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…
(Almost) Free Incentivized Exploration from Decentralized Learning Agents
Chengshuai Shi, Haifeng Xu, Wei Xiong +1
Incentivized exploration in multi-armed bandits (MAB) has witnessed increasing interests and many progresses in recent years, where a principal offers bonuses to agents to do explo…
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…
Multi-player Multi-armed Bandits with Collision-Dependent Reward Distributions
Chengshuai Shi, Cong Shen
We study a new stochastic multi-player multi-armed bandits (MP-MAB) problem, where the reward distribution changes if a collision occurs on the arm. Existing literature always assu…
Federated Multi-Armed Bandits
Chengshuai Shi, Cong Shen
Federated multi-armed bandits (FMAB) is a new bandit paradigm that parallels the federated learning (FL) framework in supervised learning. It is inspired by practical applications…
Federated Multi-armed Bandits with Personalization
Chengshuai Shi, Cong Shen, Jing Yang
A general framework of personalized federated multi-armed bandits (PF-MAB) is proposed, which is a new bandit paradigm analogous to the federated learning (FL) framework in supervi…