Simultaneously Learning Stochastic and Adversarial Bandits with General
Graph Feedback

v1v2 (latest)

Simultaneously Learning Stochastic and Adversarial Bandits with General Graph Feedback

16 June 2022

Shuai Li

ArXiv (abs)PDF HTML

Papers citing "Simultaneously Learning Stochastic and Adversarial Bandits with General Graph Feedback"

7 / 7 papers shown

Title
Pure Exploration with Feedback Graphs Alessio Russo Yichen Song Aldo Pacchiano 72 2 0 10 Mar 2025
Graph Neural Thompson Sampling Shuang Wu Arash A. Amini 96 0 0 15 Jun 2024
$A Simple and Adaptive Learning Rate for FTRL in Online Learning with Minimax Regret of $Θ(T^{2/3})$ and its Application to Best-of-Both-Worlds$ A Simple and Adaptive Learning Rate for FTRL in Online Learning with Minimax Regret of $Θ(T^{2/3})$ and its Application to Best-of-Both-Worlds Taira Tsuchiya Shinji Ito 81 0 0 30 May 2024
Best-of-three-worlds Analysis for Linear Bandits with Follow-the-regularized-leader Algorithm Fang-yuan Kong Canzhe Zhao Shuai Li 83 12 0 13 Mar 2023
A Blackbox Approach to Best of Both Worlds in Bandits and Beyond Christoph Dann Chen-Yu Wei Julian Zimmert 73 24 0 20 Feb 2023
Best-of-Both-Worlds Algorithms for Partial Monitoring Taira Tsuchiya Shinji Ito Junya Honda 52 16 0 29 Jul 2022
Nearly Optimal Best-of-Both-Worlds Algorithms for Online Learning with Feedback Graphs Shinji Ito Taira Tsuchiya Junya Honda 78 24 0 02 Jun 2022