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PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits

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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Online Sampling from Log-Concave Distributions
Holden Lee
Oren Mangoubi
Nisheeth K. Vishnoi
18
3
0
21 Feb 2019
1