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2202.02407
Cited By

An Experimental Design Approach for Regret Minimization in Logistic
Bandits

An Experimental Design Approach for Regret Minimization in Logistic Bandits

AAAI Conference on Artificial Intelligence (AAAI), 2022

4 February 2022

ArXiv (abs)PDF HTML

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

7 / 7 papers shown

Generalized Linear Bandits: Almost Optimal Regret with One-Pass Update

Generalized Linear Bandits: Almost Optimal Regret with One-Pass Update

Masashi Sugiyama

246

8

0

16 Jul 2025

A Unified Confidence Sequence for Generalized Linear Models, with Applications to Bandits

A Unified Confidence Sequence for Generalized Linear Models, with Applications to Bandits

716

23

0

19 Jul 2024

Optimal Design for Human Preference Elicitation

Optimal Design for Human Preference Elicitation

Subhojyoti Mukherjee

Kousha Kalantari

Aniket Deshmukh

Branislav Kveton

428

0

0

22 Apr 2024

Generalized Linear Bandits with Limited Adaptivity

Generalized Linear Bandits with Limited AdaptivityNeural Information Processing Systems (NeurIPS), 2024

Siddharth Barman

852

16

0

10 Apr 2024

Experimental Designs for Heteroskedastic Variance

Experimental Designs for Heteroskedastic VarianceNeural Information Processing Systems (NeurIPS), 2023

356

9

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 RewardsNeural Information Processing Systems (NeurIPS), 2023

415

1

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

295

4

0

29 Sep 2021

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