UCB-based Algorithms for Multinomial Logistic Regression Bandits

21 March 2021

Papers citing "UCB-based Algorithms for Multinomial Logistic Regression Bandits"

9 / 9 papers shown

Title
Provably Efficient Reinforcement Learning with Multinomial Logit Function Approximation Long-Fei Li Yu-Jie Zhang Peng Zhao Zhi-Hua Zhou 101 4 0 17 Jan 2025
Nearly Minimax Optimal Regret for Multinomial Logistic Bandit Joongkyu Lee Min-hwan Oh 44 6 0 16 May 2024
Exponentially Convergent Algorithms for Supervised Matrix Factorization Joowon Lee Hanbaek Lyu Weixin Yao 11 1 0 18 Nov 2023
Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion Junghyun Lee Se-Young Yun Kwang-Sung Jun 41 12 0 28 Oct 2023
Ranking with Popularity Bias: User Welfare under Self-Amplification Dynamics Guy Tennenholtz Martin Mladenov Nadav Merlis Robert L. Axtell Craig Boutilier 6 0 0 24 May 2023
Reinforcement Learning with History-Dependent Dynamic Contexts Guy Tennenholtz Nadav Merlis Lior Shani Martin Mladenov Craig Boutilier AI4CE 16 6 0 04 Feb 2023
Supervised Dictionary Learning with Auxiliary Covariates Joo-Hyun Lee Hanbaek Lyu W. Yao 22 1 0 14 Jun 2022
A Tractable Online Learning Algorithm for the Multinomial Logit Contextual Bandit Priyank Agrawal Theja Tulabandhula Vashist Avadhanula 23 12 0 28 Nov 2020
Instance-Wise Minimax-Optimal Algorithms for Logistic Bandits Marc Abeille Louis Faury Clément Calauzènes 96 37 0 23 Oct 2020