Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning

2 October 2021

Tong Zhang

Papers citing "Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning"

49 / 49 papers shown

Title
Sparse Nonparametric Contextual Bandits Hamish Flynn Julia Olkhovskaya Paul Rognon-Vael 51 0 0 20 Mar 2025
Decision Making in Hybrid Environments: A Model Aggregation Approach Haolin Liu Chen-Yu Wei Julian Zimmert 86 0 0 09 Feb 2025
An Information-Theoretic Analysis of Thompson Sampling with Infinite Action Spaces Amaury Gouverneur Borja Rodríguez Gálvez T. Oechtering Mikael Skoglund 51 0 0 04 Feb 2025
How Does Variance Shape the Regret in Contextual Bandits? Zeyu Jia Jian Qian Alexander Rakhlin Chen-Yu Wei 35 4 0 16 Oct 2024
Neural Dueling Bandits: Preference-Based Optimization with Human Feedback Arun Verma Zhongxiang Dai Xiaoqiang Lin P. Jaillet K. H. Low 32 5 0 24 Jul 2024
A Unified Confidence Sequence for Generalized Linear Models, with Applications to Bandits Junghyun Lee Se-Young Yun Kwang-Sung Jun 30 4 0 19 Jul 2024
Random Latent Exploration for Deep Reinforcement Learning Srinath Mahankali Zhang-Wei Hong Ayush Sekhari Alexander Rakhlin Pulkit Agrawal 33 3 0 18 Jul 2024
More Efficient Randomized Exploration for Reinforcement Learning via Approximate Sampling Haque Ishfaq Yixin Tan Yu Yang Qingfeng Lan Jianfeng Lu A. Rupam Mahmood Doina Precup Pan Xu 32 4 0 18 Jun 2024
From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems Jianliang He Siyu Chen Fengzhuo Zhang Zhuoran Yang LM&Ro LLMAG 40 2 0 30 May 2024
Feel-Good Thompson Sampling for Contextual Dueling Bandits Xuheng Li Heyang Zhao Quanquan Gu 40 8 0 09 Apr 2024
Prior-dependent analysis of posterior sampling reinforcement learning with function approximation Yingru Li Zhi-Quan Luo 24 1 0 17 Mar 2024
Regret Minimization via Saddle Point Optimization Johannes Kirschner Seyed Alireza Bakhtiari Kushagra Chandak Volodymyr Tkachuk Csaba Szepesvári 31 1 0 15 Mar 2024
Optimistic Information Directed Sampling Gergely Neu Matteo Papini Ludovic Schwartz 42 2 0 23 Feb 2024
Contextual Multinomial Logit Bandits with General Value Functions Mengxiao Zhang Haipeng Luo 19 1 0 12 Feb 2024
Online Iterative Reinforcement Learning from Human Feedback with General Preference Model Chen Ye Wei Xiong Yuheng Zhang Nan Jiang Tong Zhang OffRL 38 9 0 11 Feb 2024
Optimistic Thompson Sampling for No-Regret Learning in Unknown Games Yingru Li Liangqi Liu Wenqiang Pu Hao Liang Zhi-Quan Luo 29 2 0 07 Feb 2024
Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgent Yingru Li Jiawei Xu Lei Han Zhi-Quan Luo BDL OffRL 23 6 0 05 Feb 2024
On Sample-Efficient Offline Reinforcement Learning: Data Diversity, Posterior Sampling, and Beyond Thanh Nguyen-Tang Raman Arora OffRL 30 3 0 06 Jan 2024
Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint Wei Xiong Hanze Dong Chen Ye Ziqi Wang Han Zhong Heng Ji Nan Jiang Tong Zhang OffRL 38 157 0 18 Dec 2023
Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation Nikki Lijing Kuang Ming Yin Mengdi Wang Yu-Xiang Wang Yian Ma 24 6 0 29 Oct 2023
Online Learning in Contextual Second-Price Pay-Per-Click Auctions Mengxiao Zhang Haipeng Luo 27 4 0 08 Oct 2023
Bayesian Design Principles for Frequentist Sequential Learning Yunbei Xu A. Zeevi 24 11 0 01 Oct 2023
Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits Haolin Liu Chen-Yu Wei Julian Zimmert 30 9 0 02 Sep 2023
VITS : Variational Inference Thompson Sampling for contextual bandits Pierre Clavier Tom Huix Alain Durmus 25 3 0 19 Jul 2023
Efficient Model-Free Exploration in Low-Rank MDPs Zakaria Mhammedi Adam Block Dylan J. Foster Alexander Rakhlin OffRL 19 13 0 08 Jul 2023
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning Amin Karbasi Nikki Lijing Kuang Yi-An Ma Siddharth Mitra OffRL 30 5 0 15 Jun 2023
Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration Zhihan Liu Miao Lu Wei Xiong Han Zhong Haotian Hu Shenao Zhang Sirui Zheng Zhuoran Yang Zhaoran Wang OffRL 32 22 0 29 May 2023
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo Haque Ishfaq Qingfeng Lan Pan Xu A. R. Mahmood Doina Precup Anima Anandkumar Kamyar Azizzadenesheli BDL OffRL 26 20 0 29 May 2023
Thompson Sampling Regret Bounds for Contextual Bandits with sub-Gaussian rewards Amaury Gouverneur Borja Rodríguez Gálvez T. Oechtering Mikael Skoglund 24 4 0 26 Apr 2023
Initial Task Allocation for Multi-Human Multi-Robot Teams with Attention-based Deep Reinforcement Learning Ruiqi Wang Dezhong Zhao Byung-Cheol Min 22 12 0 04 Mar 2023
Leveraging Demonstrations to Improve Online Learning: Quality Matters Botao Hao Rahul Jain Tor Lattimore Benjamin Van Roy Zheng Wen 18 8 0 07 Feb 2023
Overcoming Prior Misspecification in Online Learning to Rank Javad Azizi Ofer Meshi M. Zoghi Maryam Karimzadehgan 15 1 0 25 Jan 2023
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes Chen Ye Wei Xiong Quanquan Gu Tong Zhang 23 29 0 12 Dec 2022
Eluder-based Regret for Stochastic Contextual MDPs Orin Levy Asaf B. Cassel Alon Cohen Yishay Mansour 28 5 0 27 Nov 2022
Model-Free Reinforcement Learning with the Decision-Estimation Coefficient Dylan J. Foster Noah Golowich Jian Qian Alexander Rakhlin Ayush Sekhari OffRL 30 9 0 25 Nov 2022
A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games Wei Xiong Han Zhong Chengshuai Shi Cong Shen Tong Zhang 60 18 0 04 Oct 2022
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees D. Tiapkin Denis Belomestny Daniele Calandriello Eric Moulines Rémi Munos A. Naumov Mark Rowland Michal Valko Pierre Menard 36 8 0 28 Sep 2022
A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning Christoph Dann M. Mohri Tong Zhang Julian Zimmert OffRL 16 32 0 23 Aug 2022
Contextual Bandits with Large Action Spaces: Made Practical Yinglun Zhu Dylan J. Foster John Langford Paul Mineiro 26 29 0 12 Jul 2022
Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation Christoph Dann Yishay Mansour M. Mohri Ayush Sekhari Karthik Sridharan 21 49 0 19 Jun 2022
Model-based RL with Optimistic Posterior Sampling: Structural Conditions and Sample Complexity Alekh Agarwal Tong Zhang 39 22 0 15 Jun 2022
Regret Bounds for Information-Directed Reinforcement Learning Botao Hao Tor Lattimore OffRL 39 17 0 09 Jun 2022
Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits Tianyuan Jin Pan Xu X. Xiao Anima Anandkumar 36 12 0 07 Jun 2022
Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization Hui Yuan Chengzhuo Ni Huazheng Wang Xuezhou Zhang Le Cong Csaba Szepesvári Mengdi Wang 20 2 0 05 Jun 2022
Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits Gergely Neu Julia Olkhovskaya Matteo Papini Ludovic Schwartz 31 16 0 27 May 2022
Non-Linear Reinforcement Learning in Large Action Spaces: Structural Conditions and Sample-efficiency of Posterior Sampling Alekh Agarwal Tong Zhang 24 8 0 15 Mar 2022
Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration Priyank Agrawal Jinglin Chen Nan Jiang 24 18 0 23 Oct 2020
Nonstationary Reinforcement Learning with Linear Function Approximation Huozhi Zhou Jinglin Chen L. Varshney A. Jagmohan 37 30 0 08 Oct 2020
TS-UCB: Improving on Thompson Sampling With Little to No Additional Computation Jackie Baek Vivek F. Farias 30 9 0 11 Jun 2020