Corralling a Band of Bandit Algorithms

19 December 2016

Papers citing "Corralling a Band of Bandit Algorithms"

35 / 35 papers shown

Title
Adaptive Resource Allocation for Virtualized Base Stations in O-RAN with Online Learning Michail Kalntis Georgios Iosifidis Fernando A. Kuipers 39 5 0 31 Dec 2024
Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations around Unknown Marginals Ziyi Liu Idan Attias Daniel M. Roy CML 29 0 0 01 Jul 2024
Anytime Model Selection in Linear Bandits Parnian Kassraie N. Emmenegger Andreas Krause Aldo Pacchiano 46 2 0 24 Jul 2023
Improved Regret Bounds for Online Kernel Selection under Bandit Feedback Junfan Li Shizhong Liao 17 1 0 09 Mar 2023
A Blackbox Approach to Best of Both Worlds in Bandits and Beyond Christoph Dann Chen-Yu Wei Julian Zimmert 24 22 0 20 Feb 2023
Estimating Optimal Policy Value in General Linear Contextual Bandits Jonathan Lee Weihao Kong Aldo Pacchiano Vidya Muthukumar Emma Brunskill 25 0 0 19 Feb 2023
Banker Online Mirror Descent: A Universal Approach for Delayed Online Bandit Learning Jiatai Huang Yan Dai Longbo Huang 13 6 0 25 Jan 2023
Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees Andrea Tirinzoni Matteo Papini Ahmed Touati A. Lazaric Matteo Pirotta 28 4 0 24 Oct 2022
Exploration in Linear Bandits with Rich Action Sets and its Implications for Inference Debangshu Banerjee Avishek Ghosh Sayak Ray Chowdhury Aditya Gopalan 24 9 0 23 Jul 2022
Best of Both Worlds Model Selection Aldo Pacchiano Christoph Dann Claudio Gentile 26 10 0 29 Jun 2022
Adversarial Bandits against Arbitrary Strategies Jung-hun Kim Se-Young Yun 49 0 0 30 May 2022
$Breaking the $\sqrt{T}$ Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits$ Breaking the $\sqrt{T}$ Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits Avishek Ghosh Abishek Sankararaman 27 3 0 19 May 2022
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions Jiafan He Dongruo Zhou Tong Zhang Quanquan Gu 66 46 0 13 May 2022
Corralling a Larger Band of Bandits: A Case Study on Switching Regret for Linear Bandits Haipeng Luo Mengxiao Zhang Peng Zhao Zhi-Hua Zhou 28 17 0 12 Feb 2022
Uncoupled Bandit Learning towards Rationalizability: Benchmarks, Barriers, and Algorithms Jibang Wu Haifeng Xu Fan Yao 22 1 0 10 Nov 2021
Misspecified Gaussian Process Bandit Optimization Ilija Bogunovic Andreas Krause 53 42 0 09 Nov 2021
Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structure Hsu Kao Chen-Yu Wei V. Subramanian 15 12 0 01 Nov 2021
On component interactions in two-stage recommender systems Jiri Hron K. Krauth Michael I. Jordan Niki Kilbertus CML LRM 40 31 0 28 Jun 2021
Multi-armed Bandit Algorithms on System-on-Chip: Go Frequentist or Bayesian? S. Santosh S. Darak 14 0 0 05 Jun 2021
Leveraging Good Representations in Linear Contextual Bandits Matteo Papini Andrea Tirinzoni Marcello Restelli A. Lazaric Matteo Pirotta 21 26 0 08 Apr 2021
A Simple Approach for Non-stationary Linear Bandits Peng Zhao Lijun Zhang Yuan Jiang Zhi-Hua Zhou 31 81 0 09 Mar 2021
Policy Optimization as Online Learning with Mediator Feedback Alberto Maria Metelli Matteo Papini P. DÓro Marcello Restelli OffRL 24 10 0 15 Dec 2020
Minimax Regret for Stochastic Shortest Path with Adversarial Costs and Known Transition Liyu Chen Haipeng Luo Chen-Yu Wei 21 32 0 07 Dec 2020
Efficient Contextual Bandits with Continuous Actions Maryam Majzoubi Chicheng Zhang Rajan Chari A. Krishnamurthy John Langford Aleksandrs Slivkins OffRL 29 32 0 10 Jun 2020
Regret Balancing for Bandit and RL Model Selection Yasin Abbasi-Yadkori Aldo Pacchiano My Phan 16 26 0 09 Jun 2020
Rate-adaptive model selection over a collection of black-box contextual bandit algorithms Aurélien F. Bibaut Antoine Chambaz Mark van der Laan 24 6 0 05 Jun 2020
Model selection for contextual bandits Dylan J. Foster A. Krishnamurthy Haipeng Luo OffRL 21 89 0 03 Jun 2019
Hedging the Drift: Learning to Optimize under Non-Stationarity Wang Chi Cheung D. Simchi-Levi Ruihao Zhu 21 89 0 04 Mar 2019
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting A. Krishnamurthy John Langford Aleksandrs Slivkins Chicheng Zhang OffRL 26 66 0 05 Feb 2019
Learning to Collaborate in Markov Decision Processes Goran Radanović R. Devidze David C. Parkes Adish Singla 29 33 0 23 Jan 2019
Learning to Optimize under Non-Stationarity Wang Chi Cheung D. Simchi-Levi Ruihao Zhu 22 132 0 06 Oct 2018
Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits Julian Zimmert Yevgeny Seldin AAML 18 173 0 19 Jul 2018
Best of many worlds: Robust model selection for online supervised learning Vidya Muthukumar Mitas Ray A. Sahai Peter L. Bartlett OffRL 40 8 0 22 May 2018
Efficient Contextual Bandits in Non-stationary Worlds Haipeng Luo Chen-Yu Wei Alekh Agarwal John Langford 16 127 0 05 Aug 2017
Kernel-based methods for bandit convex optimization Sébastien Bubeck Ronen Eldan Y. Lee 84 163 0 11 Jul 2016