Nearly Minimax Optimal Regret for Multinomial Logistic Bandit

16 May 2024

Papers citing "Nearly Minimax Optimal Regret for Multinomial Logistic Bandit"

7 / 7 papers shown

Title
Dynamic Assortment Selection and Pricing with Censored Preference Feedback Jung-hun Kim Min-hwan Oh 33 0 0 03 Apr 2025
Heavy-Tailed Linear Bandits: Huber Regression with One-Pass Update Jing Wang Yu-Jie Zhang Peng Zhao Zhi-Hua Zhou 51 0 0 01 Mar 2025
Learning an Optimal Assortment Policy under Observational Data Yuxuan Han Han Zhong Miao Lu Jose H. Blanchet Zhengyuan Zhou OffRL 65 0 0 10 Feb 2025
Provably Efficient Reinforcement Learning with Multinomial Logit Function Approximation Long-Fei Li Yu-Jie Zhang Peng Zhao Zhi-Hua Zhou 96 4 0 17 Jan 2025
Optimal Design for Reward Modeling in RLHF Antoine Scheid Etienne Boursier Alain Durmus Michael I. Jordan Pierre Ménard Eric Moulines Michal Valko OffRL 45 6 0 22 Oct 2024
Double Doubly Robust Thompson Sampling for Generalized Linear Contextual Bandits Wonyoung Hedge Kim Kyungbok Lee M. Paik 28 13 0 15 Sep 2022
Instance-Wise Minimax-Optimal Algorithms for Logistic Bandits Marc Abeille Louis Faury Clément Calauzènes 96 37 0 23 Oct 2020