Collaborative Linear Bandits with Adversarial Agents: Near-Optimal
Regret Bounds

Collaborative Linear Bandits with Adversarial Agents: Near-Optimal Regret Bounds

6 June 2022

George J. Pappas

Papers citing "Collaborative Linear Bandits with Adversarial Agents: Near-Optimal Regret Bounds"

11 / 11 papers shown

Title
Harnessing the Power of Federated Learning in Federated Contextual Bandits Chengshuai Shi Ruida Zhou Kun Yang Cong Shen FedML 19 0 0 26 Dec 2023
Byzantine-Resilient Decentralized Multi-Armed Bandits Jingxuan Zhu Alec Koppel Alvaro Velasquez Ji Liu 6 6 0 11 Oct 2023
Byzantine-Robust Distributed Online Learning: Taming Adversarial Participants in An Adversarial Environment Xingrong Dong Zhaoxian Wu Qing Ling Zhi Tian AAML 27 9 0 16 Jul 2023
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions Jiafan He Dongruo Zhou Tong Zhang Quanquan Gu 61 46 0 13 May 2022
Federated Multi-Armed Bandits Under Byzantine Attacks Artun Saday Ilker Demirel Yiğit Yıldırım Cem Tekin AAML 23 13 0 09 May 2022
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients A. Mitra Rayana H. Jaafar George J. Pappas Hamed Hassani FedML 55 157 0 14 Feb 2021
Multi-Agent Multi-Armed Bandits with Limited Communication Mridul Agarwal Vaneet Aggarwal Kamyar Azizzadenesheli 31 32 0 10 Feb 2021
Federated Bandit: A Gossiping Approach Zhaowei Zhu Jingxuan Zhu Ji Liu Yang Liu FedML 134 83 0 24 Oct 2020
Bayesian Algorithms for Decentralized Stochastic Bandits Anusha Lalitha Andrea J. Goldsmith 22 15 0 20 Oct 2020
Distributed Cooperative Decision Making in Multi-agent Multi-armed Bandits Peter Landgren Vaibhav Srivastava Naomi Ehrich Leonard 67 68 0 03 Mar 2020
Bounded regret in stochastic multi-armed bandits Sébastien Bubeck Vianney Perchet Philippe Rigollet 56 90 0 06 Feb 2013