Reward Teaching for Federated Multi-armed Bandits
Author:
Affiliation:
1. University of Virginia,Charlottesville,VA,USA,22904
2. The Hong Kong University of Science and Technology,Hong Kong
3. The Pennsylvania State University, University Park,University Park,USA,16802
Funder
National Science Foundation
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10206429/10206441/10206444.pdf?arnumber=10206444
Reference22 articles.
1. Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems
2. Data poisoning attacks on stochastic bandits;liu;International Conference on Machine Learning,2019
3. Adversarial attacks on stochastic bandits;jun;Advances in neural information processing systems,2018
4. Minimax policies for adversarial and stochastic bandits;audibert;COLT,2009
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Reward Teaching for Federated Multi-armed Bandits;2023 IEEE International Symposium on Information Theory (ISIT);2023-06-25
2. Reward Teaching for Federated Multiarmed Bandits;IEEE Transactions on Signal Processing;2023
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