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Improved Regret Bounds of (Multinomial) Logistic Bandits via
  Regret-to-Confidence-Set Conversion

Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion

28 October 2023
Junghyun Lee
Se-Young Yun
Kwang-Sung Jun
ArXivPDFHTML

Papers citing "Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion"

16 / 16 papers shown
Title
Neural Logistic Bandits
Neural Logistic Bandits
Seoungbin Bae
Dabeen Lee
127
0
0
04 May 2025
Confidence Sequences for Generalized Linear Models via Regret Analysis
Confidence Sequences for Generalized Linear Models via Regret Analysis
Eugenio Clerico
Hamish Flynn
W. Kotłowski
Gergely Neu
27
0
0
23 Apr 2025
Optimal Design for Reward Modeling in RLHF
Optimal Design for Reward Modeling in RLHF
Antoine Scheid
Etienne Boursier
Alain Durmus
Michael I. Jordan
Pierre Ménard
Eric Moulines
Michal Valko
OffRL
40
5
0
22 Oct 2024
A Unified Confidence Sequence for Generalized Linear Models, with Applications to Bandits
A Unified Confidence Sequence for Generalized Linear Models, with Applications to Bandits
Junghyun Lee
Se-Young Yun
Kwang-Sung Jun
28
4
0
19 Jul 2024
Bandits with Preference Feedback: A Stackelberg Game Perspective
Bandits with Preference Feedback: A Stackelberg Game Perspective
Barna Pásztor
Parnian Kassraie
Andreas Krause
40
2
0
24 Jun 2024
Nearly Minimax Optimal Regret for Multinomial Logistic Bandit
Nearly Minimax Optimal Regret for Multinomial Logistic Bandit
Joongkyu Lee
Min-hwan Oh
33
6
0
16 May 2024
Generalized Linear Bandits with Limited Adaptivity
Generalized Linear Bandits with Limited Adaptivity
Ayush Sawarni
Nirjhar Das
Siddharth Barman
Gaurav Sinha
32
3
0
10 Apr 2024
Tighter Confidence Bounds for Sequential Kernel Regression
Tighter Confidence Bounds for Sequential Kernel Regression
H. Flynn
David Reeb
28
3
0
19 Mar 2024
Active Preference Optimization for Sample Efficient RLHF
Active Preference Optimization for Sample Efficient RLHF
Nirjhar Das
Souradip Chakraborty
Aldo Pacchiano
Sayak Ray Chowdhury
27
13
0
16 Feb 2024
Noise-Adaptive Confidence Sets for Linear Bandits and Application to
  Bayesian Optimization
Noise-Adaptive Confidence Sets for Linear Bandits and Application to Bayesian Optimization
Kwang-Sung Jun
Jungtaek Kim
27
2
0
12 Feb 2024
Efficient Frameworks for Generalized Low-Rank Matrix Bandit Problems
Efficient Frameworks for Generalized Low-Rank Matrix Bandit Problems
Yue Kang
Cho-Jui Hsieh
T. C. Lee
37
17
0
14 Jan 2024
Game-theoretic statistics and safe anytime-valid inference
Game-theoretic statistics and safe anytime-valid inference
Aaditya Ramdas
Peter Grünwald
V. Vovk
Glenn Shafer
38
118
0
04 Oct 2022
Double Doubly Robust Thompson Sampling for Generalized Linear Contextual
  Bandits
Double Doubly Robust Thompson Sampling for Generalized Linear Contextual Bandits
Wonyoung Hedge Kim
Kyungbok Lee
M. Paik
25
13
0
15 Sep 2022
Mixability made efficient: Fast online multiclass logistic regression
Mixability made efficient: Fast online multiclass logistic regression
Rémi Jézéquel
Pierre Gaillard
Alessandro Rudi
33
10
0
08 Oct 2021
Apple Tasting Revisited: Bayesian Approaches to Partially Monitored
  Online Binary Classification
Apple Tasting Revisited: Bayesian Approaches to Partially Monitored Online Binary Classification
James A. Grant
David S. Leslie
34
3
0
29 Sep 2021
Instance-Wise Minimax-Optimal Algorithms for Logistic Bandits
Instance-Wise Minimax-Optimal Algorithms for Logistic Bandits
Marc Abeille
Louis Faury
Clément Calauzènes
96
37
0
23 Oct 2020
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