Adaptive Best-of-Both-Worlds Algorithm for Heavy-Tailed Multi-Armed Bandits

28 January 2022

Papers citing "Adaptive Best-of-Both-Worlds Algorithm for Heavy-Tailed Multi-Armed Bandits"

9 / 9 papers shown

Title
uniINF: Best-of-Both-Worlds Algorithm for Parameter-Free Heavy-Tailed MABs Yu Chen Jiatai Huang Yan Dai Longbo Huang 29 0 0 04 Oct 2024
Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds Shinji Ito Taira Tsuchiya Junya Honda 38 3 0 01 Mar 2024
Fast UCB-type algorithms for stochastic bandits with heavy and super heavy symmetric noise Yuriy Dorn Aleksandr Katrutsa Ilgam Latypov Andrey Pudovikov 17 2 0 10 Feb 2024
Efficient Algorithms for Generalized Linear Bandits with Heavy-tailed Rewards Bo Xue Yimu Wang Yuanyu Wan Jinfeng Yi Lijun Zhang 33 3 0 28 Oct 2023
Towards Robust Offline Reinforcement Learning under Diverse Data Corruption Rui Yang Han Zhong Jiawei Xu Amy Zhang Chong Zhang Lei Han Tong Zhang OffRL OnRL 41 15 0 19 Oct 2023
$(ε, u)$ -Adaptive Regret Minimization in Heavy-Tailed Bandits Gianmarco Genalti Lupo Marsigli Nicola Gatti Alberto Maria Metelli 16 0 0 04 Oct 2023
Implicitly normalized forecaster with clipping for linear and non-linear heavy-tailed multi-armed bandits Yuriy Dorn Kornilov Nikita N. Kutuzov A. Nazin Eduard A. Gorbunov Alexander Gasnikov 26 5 0 11 May 2023
A Blackbox Approach to Best of Both Worlds in Bandits and Beyond Christoph Dann Chen-Yu Wei Julian Zimmert 19 22 0 20 Feb 2023
Best-of-Both-Worlds Algorithms for Partial Monitoring Taira Tsuchiya Shinji Ito Junya Honda 11 16 0 29 Jul 2022