A minimax and asymptotically optimal algorithm for stochastic bandits

A minimax and asymptotically optimal algorithm for stochastic bandits

23 February 2017

Pierre Ménard

Aurélien Garivier

Papers citing "A minimax and asymptotically optimal algorithm for stochastic bandits"

12 / 12 papers shown

Title
On Lai's Upper Confidence Bound in Multi-Armed Bandits Huachen Ren Cun-Hui Zhang 28 1 0 03 Oct 2024
Efficient and Adaptive Posterior Sampling Algorithms for Bandits Bingshan Hu Zhiming Huang Tianyue H. Zhang Mathias Lécuyer Nidhi Hegde 23 0 0 02 May 2024
Multi-Armed Bandits with Abstention Junwen Yang Tianyuan Jin Vincent Y. F. Tan 31 0 0 23 Feb 2024
Optimal Batched Best Arm Identification Tianyuan Jin Yu Yang Jing Tang Xiaokui Xiao Pan Xu 44 3 0 21 Oct 2023
VITS : Variational Inference Thompson Sampling for contextual bandits Pierre Clavier Tom Huix Alain Durmus 29 3 0 19 Jul 2023
Kullback-Leibler Maillard Sampling for Multi-armed Bandits with Bounded Rewards Hao Qin Kwang-Sung Jun Chicheng Zhang 35 0 0 28 Apr 2023
Top Two Algorithms Revisited Marc Jourdan Rémy Degenne Dorian Baudry R. D. Heide E. Kaufmann 26 38 0 13 Jun 2022
Truncated LinUCB for Stochastic Linear Bandits Yanglei Song Meng zhou 52 0 0 23 Feb 2022
Maillard Sampling: Boltzmann Exploration Done Optimally Jieming Bian Kwang-Sung Jun 19 12 0 05 Nov 2021
KL-UCB-switch: optimal regret bounds for stochastic bandits from both a distribution-dependent and a distribution-free viewpoints Aurélien Garivier Hédi Hadiji Pierre Menard Gilles Stoltz 28 32 0 14 May 2018
Learning the distribution with largest mean: two bandit frameworks E. Kaufmann Aurélien Garivier 24 19 0 31 Jan 2017
On Bayesian index policies for sequential resource allocation E. Kaufmann 41 84 0 06 Jan 2016