Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits

19 July 2018

Papers citing "Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits"

40 / 40 papers shown

Title
Functional multi-armed bandit and the best function identification problems Yuriy Dorn Aleksandr Katrutsa Ilgam Latypov Anastasiia Soboleva 32 0 0 01 Mar 2025
Revisiting Online Learning Approach to Inverse Linear Optimization: A Fenchel $-$ Young Loss Perspective and Gap-Dependent Regret Analysis Shinsaku Sakaue Han Bao Taira Tsuchiya 37 1 0 23 Jan 2025
Beyond Minimax Rates in Group Distributionally Robust Optimization via a Novel Notion of Sparsity Quan Nguyen Nishant A. Mehta Cristóbal Guzmán 34 0 0 01 Oct 2024
Optimism in the Face of Ambiguity Principle for Multi-Armed Bandits Mengmeng Li Daniel Kuhn Bahar Taşkesen 39 0 0 30 Sep 2024
Towards the Transferability of Rewards Recovered via Regularized Inverse Reinforcement Learning Andreas Schlaginhaufen Maryam Kamgarpour OffRL 23 1 0 03 Jun 2024
$A Simple and Adaptive Learning Rate for FTRL in Online Learning with Minimax Regret of $Θ(T^{2/3})$ and its Application to Best-of-Both-Worlds$ A Simple and Adaptive Learning Rate for FTRL in Online Learning with Minimax Regret of $Θ(T^{2/3})$ and its Application to Best-of-Both-Worlds Taira Tsuchiya Shinji Ito 26 0 0 30 May 2024
LC-Tsallis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits Masahiro Kato Shinji Ito 36 0 0 05 Mar 2024
Best-of-Both-Worlds Linear Contextual Bandits Masahiro Kato Shinji Ito 53 0 0 27 Dec 2023
CRIMED: Lower and Upper Bounds on Regret for Bandits with Unbounded Stochastic Corruption Shubhada Agrawal Timothée Mathieu D. Basu Odalric-Ambrym Maillard 25 2 0 28 Sep 2023
A Best-of-both-worlds Algorithm for Bandits with Delayed Feedback with Robustness to Excessive Delays Saeed Masoudian Julian Zimmert Yevgeny Seldin 42 3 0 21 Aug 2023
On the Minimax Regret for Online Learning with Feedback Graphs Khaled Eldowa Emmanuel Esposito Tommaso Cesari Nicolò Cesa-Bianchi 30 8 0 24 May 2023
Kullback-Leibler Maillard Sampling for Multi-armed Bandits with Bounded Rewards Hao Qin Kwang-Sung Jun Chicheng Zhang 30 0 0 28 Apr 2023
Bandits for Sponsored Search Auctions under Unknown Valuation Model: Case Study in E-Commerce Advertising Danil Provodin Jérémie Joudioux E. Duryev 24 0 0 31 Mar 2023
Improved Regret Bounds for Online Kernel Selection under Bandit Feedback Junfan Li Shizhong Liao 19 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
Learning in quantum games Kyriakos Lotidis P. Mertikopoulos Nicholas Bambos 16 6 0 05 Feb 2023
Efficient Node Selection in Private Personalized Decentralized Learning Edvin Listo Zec Johan Ostman Olof Mogren D. Gillblad 29 1 0 30 Jan 2023
Pareto Regret Analyses in Multi-objective Multi-armed Bandit Mengfan Xu Diego Klabjan 19 7 0 01 Dec 2022
On Regret-optimal Cooperative Nonstochastic Multi-armed Bandits Jialin Yi Milan Vojnović 21 3 0 30 Nov 2022
Anytime-valid off-policy inference for contextual bandits Ian Waudby-Smith Lili Wu Aaditya Ramdas Nikos Karampatziakis Paul Mineiro OffRL 43 25 0 19 Oct 2022
Entropy Regularization for Population Estimation Ben Chugg Peter Henderson Jacob Goldin Daniel E. Ho 28 3 0 24 Aug 2022
Learning in Stackelberg Games with Non-myopic Agents Nika Haghtalab Thodoris Lykouris Sloan Nietert Alexander Wei 17 29 0 19 Aug 2022
Best of Both Worlds Model Selection Aldo Pacchiano Christoph Dann Claudio Gentile 26 10 0 29 Jun 2022
Adversarially Robust Multi-Armed Bandit Algorithm with Variance-Dependent Regret Bounds Shinji Ito Taira Tsuchiya Junya Honda AAML 15 16 0 14 Jun 2022
Nearly Optimal Best-of-Both-Worlds Algorithms for Online Learning with Feedback Graphs Shinji Ito Taira Tsuchiya Junya Honda 22 24 0 02 Jun 2022
A Near-Optimal Best-of-Both-Worlds Algorithm for Online Learning with Feedback Graphs Chloé Rouyer Dirk van der Hoeven Nicolò Cesa-Bianchi Yevgeny Seldin 21 15 0 01 Jun 2022
Versatile Dueling Bandits: Best-of-both-World Analyses for Online Learning from Preferences Aadirupa Saha Pierre Gaillard 36 8 0 14 Feb 2022
Mean-based Best Arm Identification in Stochastic Bandits under Reward Contamination Arpan Mukherjee A. Tajer Pin-Yu Chen Payel Das AAML FedML 26 9 0 14 Nov 2021
When Are Linear Stochastic Bandits Attackable? Huazheng Wang Haifeng Xu Hongning Wang AAML 37 10 0 18 Oct 2021
On Optimal Robustness to Adversarial Corruption in Online Decision Problems Shinji Ito 42 22 0 22 Sep 2021
Finite-time Analysis of Globally Nonstationary Multi-Armed Bandits Junpei Komiyama Edouard Fouché Junya Honda 33 5 0 23 Jul 2021
Bayesian decision-making under misspecified priors with applications to meta-learning Max Simchowitz Christopher Tosh A. Krishnamurthy Daniel J. Hsu Thodoris Lykouris Miroslav Dudík Robert Schapire 22 49 0 03 Jul 2021
Cooperative Stochastic Multi-agent Multi-armed Bandits Robust to Adversarial Corruptions Junyan Liu Shuai Li Dapeng Li 15 6 0 08 Jun 2021
The best of both worlds: stochastic and adversarial episodic MDPs with unknown transition Tiancheng Jin Longbo Huang Haipeng Luo 24 40 0 08 Jun 2021
Improved Analysis of the Tsallis-INF Algorithm in Stochastically Constrained Adversarial Bandits and Stochastic Bandits with Adversarial Corruptions Saeed Masoudian Yevgeny Seldin 14 14 0 23 Mar 2021
An Algorithm for Stochastic and Adversarial Bandits with Switching Costs Chloé Rouyer Yevgeny Seldin Nicolò Cesa-Bianchi AAML 13 23 0 19 Feb 2021
Bandits with adversarial scaling Thodoris Lykouris Vahab Mirrokni R. Leme 6 14 0 04 Mar 2020
Nonstochastic Multiarmed Bandits with Unrestricted Delays Tobias Sommer Thune Nicolò Cesa-Bianchi Yevgeny Seldin 15 52 0 03 Jun 2019
Better Algorithms for Stochastic Bandits with Adversarial Corruptions Anupam Gupta Tomer Koren Kunal Talwar AAML 8 150 0 22 Feb 2019
KL-UCB-switch: optimal regret bounds for stochastic bandits from both a distribution-dependent and a distribution-free viewpoints Aurélien Garivier Hédi Hadiji Pierre Menard Gilles Stoltz 18 32 0 14 May 2018