295 citations · 405 across the 47 of their papers we have counts for
8 papers · 1 filter
Learning for Robust Combinatorial Optimization: Algorithm and Application
Zhihui Shao, Jianyi Yang, Cong Shen +1
Learning to optimize (L2O) has recently emerged as a promising approach to solving optimization problems by exploiting the strong prediction power of neural networks and offering l…
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…
Federated Learning over Noisy Channels: Convergence Analysis and Design Examples
Xizixiang Wei, Cong Shen
Does Federated Learning (FL) work when both uplink and downlink communications have errors? How much communication noise can FL handle and what is its impact to the learning perfor…