Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits

27 May 2022

Papers citing "Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits"

31 / 31 papers shown

Title
On Bits and Bandits: Quantifying the Regret-Information Trade-off Itai Shufaro Nadav Merlis Nir Weinberger Shie Mannor 94 0 0 26 May 2024
Contextual Information-Directed Sampling Botao Hao Tor Lattimore Chao Qin 54 14 0 22 May 2022
An Analysis of Ensemble Sampling Chao Qin Zheng Wen Xiuyuan Lu Benjamin Van Roy 56 22 0 02 Mar 2022
Jointly Efficient and Optimal Algorithms for Logistic Bandits Louis Faury Marc Abeille Kwang-Sung Jun Clément Calauzènes 45 20 0 06 Jan 2022
Gaussian Imagination in Bandit Learning Yueyang Liu Adithya M. Devraj Benjamin Van Roy Kuang Xu 47 7 0 06 Jan 2022
The Statistical Complexity of Interactive Decision Making Dylan J. Foster Sham Kakade Jian Qian Alexander Rakhlin 137 177 0 27 Dec 2021
Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning Tong Zhang 32 64 0 02 Oct 2021
Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination Dylan J. Foster A. Krishnamurthy 58 44 0 05 Jul 2021
Reinforcement Learning, Bit by Bit Xiuyuan Lu Benjamin Van Roy Vikranth Dwaracherla M. Ibrahimi Ian Osband Zheng Wen 40 70 0 06 Mar 2021
Provable Model-based Nonlinear Bandit and Reinforcement Learning: Shelve Optimism, Embrace Virtual Curvature Kefan Dong Jiaqi Yang Tengyu Ma 43 33 0 08 Feb 2021
Instance-Wise Minimax-Optimal Algorithms for Logistic Bandits Marc Abeille Louis Faury Clément Calauzènes 103 37 0 23 Oct 2020
Information Directed Sampling for Linear Partial Monitoring Johannes Kirschner Tor Lattimore Andreas Krause 41 46 0 25 Feb 2020
Improved Optimistic Algorithms for Logistic Bandits Louis Faury Marc Abeille Clément Calauzènes Olivier Fercoq 47 92 0 18 Feb 2020
Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles Dylan J. Foster Alexander Rakhlin 139 207 0 12 Feb 2020
Randomized Exploration in Generalized Linear Bandits Branislav Kveton Manzil Zaheer Csaba Szepesvári Lihong Li Mohammad Ghavamzadeh Craig Boutilier 25 96 0 21 Jun 2019
On the Performance of Thompson Sampling on Logistic Bandits Shi Dong Tengyu Ma Benjamin Van Roy 24 39 0 12 May 2019
First-Order Bayesian Regret Analysis of Thompson Sampling Sébastien Bubeck Mark Sellke 32 16 0 02 Feb 2019
An Information-Theoretic Analysis for Thompson Sampling with Many Actions Shi Dong Benjamin Van Roy 23 49 0 30 May 2018
Analysis of Thompson Sampling for Graphical Bandits Without the Graphs Fang Liu Zizhan Zheng Ness B. Shroff 25 14 0 23 May 2018
PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits Bianca Dumitrascu Karen Feng Barbara E. Engelhardt 30 41 0 18 May 2018
Information Directed Sampling and Bandits with Heteroscedastic Noise Johannes Kirschner Andreas Krause 45 122 0 29 Jan 2018
Ensemble Sampling Xiuyuan Lu Benjamin Van Roy 73 119 0 20 May 2017
Learning to Optimize via Information-Directed Sampling Daniel Russo Benjamin Van Roy 92 278 0 21 Mar 2014
An Information-Theoretic Analysis of Thompson Sampling Daniel Russo Benjamin Van Roy 79 423 0 21 Mar 2014
Generalized Thompson Sampling for Contextual Bandits Lihong Li 41 23 0 27 Oct 2013
Learning to Optimize Via Posterior Sampling Daniel Russo Benjamin Van Roy 122 697 0 11 Jan 2013
Further Optimal Regret Bounds for Thompson Sampling Shipra Agrawal Navin Goyal 64 443 0 15 Sep 2012
Thompson Sampling for Contextual Bandits with Linear Payoffs Shipra Agrawal Navin Goyal 115 993 0 15 Sep 2012
Thompson Sampling: An Asymptotically Optimal Finite Time Analysis E. Kaufmann N. Korda Rémi Munos 81 585 0 18 May 2012
Efficient Optimal Learning for Contextual Bandits Miroslav Dudík Daniel J. Hsu Satyen Kale Nikos Karampatziakis John Langford L. Reyzin Tong Zhang 97 300 0 13 Jun 2011
Contextual Bandit Algorithms with Supervised Learning Guarantees A. Beygelzimer John Langford Lihong Li L. Reyzin Robert Schapire OffRL 111 324 0 22 Feb 2010