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An Experimental Design Approach for Regret Minimization in Logistic
  Bandits

An Experimental Design Approach for Regret Minimization in Logistic Bandits

4 February 2022
Blake Mason
Kwang-Sung Jun
Lalit P. Jain
ArXivPDFHTML

Papers citing "An Experimental Design Approach for Regret Minimization in Logistic Bandits"

7 / 7 papers shown
Title
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
33
4
0
19 Jul 2024
Optimal Design for Human Feedback
Optimal Design for Human Feedback
Subhojyoti Mukherjee
Anusha Lalitha
Kousha Kalantari
Aniket Deshmukh
Ge Liu
Yifei Ma
B. Kveton
48
5
0
22 Apr 2024
Generalized Linear Bandits with Limited Adaptivity
Generalized Linear Bandits with Limited Adaptivity
Ayush Sawarni
Nirjhar Das
Siddharth Barman
Gaurav Sinha
42
3
0
10 Apr 2024
Experimental Designs for Heteroskedastic Variance
Experimental Designs for Heteroskedastic Variance
Justin Weltz
Tanner Fiez
Alex Volfovsky
Eric B. Laber
Blake Mason
Houssam Nassif
Lalit P. Jain
29
3
0
06 Oct 2023
Kullback-Leibler Maillard Sampling for Multi-armed Bandits with Bounded
  Rewards
Kullback-Leibler Maillard Sampling for Multi-armed Bandits with Bounded Rewards
Hao Qin
Kwang-Sung Jun
Chicheng Zhang
32
0
0
28 Apr 2023
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
44
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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