Improved Analysis of the Tsallis-INF Algorithm in Stochastically Constrained Adversarial Bandits and Stochastic Bandits with Adversarial Corruptions

23 March 2021

Papers citing "Improved Analysis of the Tsallis-INF Algorithm in Stochastically Constrained Adversarial Bandits and Stochastic Bandits with Adversarial Corruptions"

8 / 8 papers shown

Title
$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 26 0 0 30 May 2024
LC-Tsallis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits Masahiro Kato Shinji Ito 36 0 0 05 Mar 2024
Best-of-Both-Worlds Linear Contextual Bandits Masahiro Kato Shinji Ito 53 0 0 27 Dec 2023
A Blackbox Approach to Best of Both Worlds in Bandits and Beyond Christoph Dann Chen-Yu Wei Julian Zimmert 24 22 0 20 Feb 2023
Adversarially Robust Multi-Armed Bandit Algorithm with Variance-Dependent Regret Bounds Shinji Ito Taira Tsuchiya Junya Honda AAML 23 16 0 14 Jun 2022
A Near-Optimal Best-of-Both-Worlds Algorithm for Online Learning with Feedback Graphs Chloé Rouyer Dirk van der Hoeven Nicolò Cesa-Bianchi Yevgeny Seldin 23 15 0 01 Jun 2022
When Are Linear Stochastic Bandits Attackable? Huazheng Wang Haifeng Xu Hongning Wang AAML 37 10 0 18 Oct 2021
On Optimal Robustness to Adversarial Corruption in Online Decision Problems Shinji Ito 42 22 0 22 Sep 2021