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1805.07458
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PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits
18 May 2018
Bianca Dumitrascu
Karen Feng
Barbara E. Engelhardt
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Papers citing
"PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits"
22 / 22 papers shown
Title
Computationally and Sample Efficient Safe Reinforcement Learning Using Adaptive Conformal Prediction
Hao Zhou
Yanze Zhang
Wenhao Luo
39
0
0
22 Mar 2025
Clustering Context in Off-Policy Evaluation
Daniel Guzman-Olivares
Philipp Schmidt
Jacek Golebiowski
Artur Bekasov
CML
OffRL
51
0
0
28 Feb 2025
Stabilizing the Kumaraswamy Distribution
Max Wasserman
Gonzalo Mateos
BDL
47
0
0
01 Oct 2024
The Power of Active Multi-Task Learning in Reinforcement Learning from Human Feedback
Ruitao Chen
Liwei Wang
72
1
0
18 May 2024
Thompson Sampling in Partially Observable Contextual Bandits
Hongju Park
Mohamad Kazem Shirani Faradonbeh
31
2
0
15 Feb 2024
Thompson sampling for zero-inflated count outcomes with an application to the Drink Less mobile health study
Xueqing Liu
Nina Deliu
Tanujit Chakraborty
Lauren Bell
Bibhas Chakraborty
16
1
0
24 Nov 2023
Overcoming Prior Misspecification in Online Learning to Rank
Javad Azizi
Ofer Meshi
M. Zoghi
Maryam Karimzadehgan
35
1
0
25 Jan 2023
Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits
Gergely Neu
Julia Olkhovskaya
Matteo Papini
Ludovic Schwartz
33
16
0
27 May 2022
Reward-Biased Maximum Likelihood Estimation for Neural Contextual Bandits
Yu-Heng Hung
Ping-Chun Hsieh
18
2
0
08 Mar 2022
An Experimental Design Approach for Regret Minimization in Logistic Bandits
Blake Mason
Kwang-Sung Jun
Lalit P. Jain
23
10
0
04 Feb 2022
Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction
Kailun Wu
Zhangming Chan
Weijie Bian
Lejian Ren
Shiming Xiang
Shuguang Han
Hongbo Deng
Bo Zheng
16
12
0
21 Dec 2021
Apple Tasting Revisited: Bayesian Approaches to Partially Monitored Online Binary Classification
James A. Grant
David S. Leslie
44
3
0
29 Sep 2021
Metadata-based Multi-Task Bandits with Bayesian Hierarchical Models
Runzhe Wan
Linjuan Ge
Rui Song
36
28
0
13 Aug 2021
Regret Bounds for Generalized Linear Bandits under Parameter Drift
Louis Faury
Yoan Russac
Marc Abeille
Clément Calauzènes
15
10
0
09 Mar 2021
Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning
Chao Du
Zhifeng Gao
Shuo Yuan
Lining Gao
Z. Li
Yifan Zeng
Xiaoqiang Zhu
Jian Xu
Kun Gai
Kuang-chih Lee
25
18
0
25 Nov 2020
Instance-Wise Minimax-Optimal Algorithms for Logistic Bandits
Marc Abeille
Louis Faury
Clément Calauzènes
96
37
0
23 Oct 2020
Reward-Biased Maximum Likelihood Estimation for Linear Stochastic Bandits
Yu-Heng Hung
Ping-Chun Hsieh
Xi Liu
P. R. Kumar
16
15
0
08 Oct 2020
Effects of Model Misspecification on Bayesian Bandits: Case Studies in UX Optimization
Mack Sweeney
M. Adelsberg
Kathryn B. Laskey
C. Domeniconi
21
1
0
07 Oct 2020
An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling
Qin Ding
Cho-Jui Hsieh
James Sharpnack
25
37
0
07 Jun 2020
Improved Optimistic Algorithms for Logistic Bandits
Louis Faury
Marc Abeille
Clément Calauzènes
Olivier Fercoq
17
85
0
18 Feb 2020
Dueling Posterior Sampling for Preference-Based Reinforcement Learning
Ellen R. Novoseller
Yibing Wei
Yanan Sui
Yisong Yue
J. W. Burdick
25
59
0
04 Aug 2019
Online Sampling from Log-Concave Distributions
Holden Lee
Oren Mangoubi
Nisheeth K. Vishnoi
18
3
0
21 Feb 2019
1