Maillard Sampling: Boltzmann Exploration Done Optimally

5 November 2021

Papers citing "Maillard Sampling: Boltzmann Exploration Done Optimally"

10 / 10 papers shown

Title
Connecting Thompson Sampling and UCB: Towards More Efficient Trade-offs Between Privacy and Regret Bingshan Hu Zhiming Huang Tianyue H. Zhang Mathias Lécuyer Nidhi Hegde 17 0 0 05 May 2025
QuACK: A Multipurpose Queuing Algorithm for Cooperative $k$ -Armed Bandits Benjamin Howson Sarah Filippi Ciara Pike-Burke 34 1 0 31 Oct 2024
Indexed Minimum Empirical Divergence-Based Algorithms for Linear Bandits Jie Bian Vincent Y. F. Tan FedML 19 0 0 24 May 2024
Efficient and Adaptive Posterior Sampling Algorithms for Bandits Bingshan Hu Zhiming Huang Tianyue H. Zhang Mathias Lécuyer Nidhi Hegde 18 0 0 02 May 2024
Allocating Divisible Resources on Arms with Unknown and Random Rewards Ningyuan Chen Wenhao Li 14 0 0 28 Jun 2023
Kullback-Leibler Maillard Sampling for Multi-armed Bandits with Bounded Rewards Hao Qin Kwang-Sung Jun Chicheng Zhang 27 0 0 28 Apr 2023
A General Recipe for the Analysis of Randomized Multi-Armed Bandit Algorithms Dorian Baudry Kazuya Suzuki Junya Honda 23 4 0 10 Mar 2023
Overcoming Prior Misspecification in Online Learning to Rank Javad Azizi Ofer Meshi M. Zoghi Maryam Karimzadehgan 13 1 0 25 Jan 2023
Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits Tianyuan Jin Pan Xu X. Xiao Anima Anandkumar 30 12 0 07 Jun 2022
Near-Optimal Algorithms for Differentially Private Online Learning in a Stochastic Environment Bingshan Hu Zhiming Huang Nishant A. Mehta Nidhi Hegde FedML 12 1 0 16 Feb 2021