A Blackbox Approach to Best of Both Worlds in Bandits and Beyond

20 February 2023

Papers citing "A Blackbox Approach to Best of Both Worlds in Bandits and Beyond"

24 / 24 papers shown

Title
A Near-optimal, Scalable and Corruption-tolerant Framework for Stochastic Bandits: From Single-Agent to Multi-Agent and Beyond Zicheng Hu Cheng Chen 65 0 0 11 Feb 2025
Tracking Most Significant Shifts in Infinite-Armed Bandits Joe Suk Jung-hun Kim 48 0 0 31 Jan 2025
A Model Selection Approach for Corruption Robust Reinforcement Learning Chen-Yu Wei Christoph Dann Julian Zimmert 77 44 0 31 Dec 2024
How Does Variance Shape the Regret in Contextual Bandits? Zeyu Jia Jian Qian Alexander Rakhlin Chen-Yu Wei 25 4 0 16 Oct 2024
Corruption-Robust Linear Bandits: Minimax Optimality and Gap-Dependent Misspecification Haolin Liu Artin Tajdini Andrew Wagenmaker Chen-Yu Wei 16 0 0 10 Oct 2024
uniINF: Best-of-Both-Worlds Algorithm for Parameter-Free Heavy-Tailed MABs Yu Chen Jiatai Huang Yan Dai Longbo Huang 24 0 0 04 Oct 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 16 0 0 30 May 2024
LC-Tsallis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits Masahiro Kato Shinji Ito 21 0 0 05 Mar 2024
Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds Shinji Ito Taira Tsuchiya Junya Honda 28 3 0 01 Mar 2024
Information Capacity Regret Bounds for Bandits with Mediator Feedback Khaled Eldowa Nicolò Cesa-Bianchi Alberto Maria Metelli Marcello Restelli 15 1 0 15 Feb 2024
Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring Taira Tsuchiya Shinji Ito Junya Honda 14 1 0 13 Feb 2024
Efficient Contextual Bandits with Uninformed Feedback Graphs Mengxiao Zhang Yuheng Zhang Haipeng Luo Paul Mineiro 17 4 0 12 Feb 2024
Best-of-Both-Worlds Linear Contextual Bandits Masahiro Kato Shinji Ito 26 0 0 27 Dec 2023
Best-of-Both-Worlds Algorithms for Linear Contextual Bandits Yuko Kuroki Alberto Rumi Taira Tsuchiya Fabio Vitale Nicolò Cesa-Bianchi 17 5 0 24 Dec 2023
Towards Optimal Regret in Adversarial Linear MDPs with Bandit Feedback Haolin Liu Chen-Yu Wei Julian Zimmert 13 6 0 17 Oct 2023
Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits Haolin Liu Chen-Yu Wei Julian Zimmert 15 9 0 02 Sep 2023
On Interpolating Experts and Multi-Armed Bandits Houshuang Chen Yuchen He Chihao Zhang 20 4 0 14 Jul 2023
Stability-penalty-adaptive follow-the-regularized-leader: Sparsity, game-dependency, and best-of-both-worlds Taira Tsuchiya Shinji Ito Junya Honda 14 7 0 26 May 2023
On the Minimax Regret for Online Learning with Feedback Graphs Khaled Eldowa Emmanuel Esposito Tommaso Cesari Nicolò Cesa-Bianchi 17 8 0 24 May 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
Accelerated Rates between Stochastic and Adversarial Online Convex Optimization Sarah Sachs Hédi Hadiji T. Erven Cristóbal Guzmán 21 6 0 06 Mar 2023
Best-of-Three-Worlds Linear Bandit Algorithm with Variance-Adaptive Regret Bounds Shinji Ito Kei Takemura 27 8 0 24 Feb 2023
A Best-of-Both-Worlds Algorithm for Bandits with Delayed Feedback Saeed Masoudian Julian Zimmert Yevgeny Seldin 31 18 0 29 Jun 2022
On Optimal Robustness to Adversarial Corruption in Online Decision Problems Shinji Ito 32 22 0 22 Sep 2021