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LC-Tsallis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits
5 March 2024
Masahiro Kato
Shinji Ito
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Papers citing
"LC-Tsallis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits"
33 / 33 papers shown
FLEET: Formal Language-Grounded Scheduling for Heterogeneous Robot Teams
Corban G. Rivera
Grayson Byrd
Meghan Booker
Bethany Kemp
Allison Gaines
Emma Holmes
James Uplinger
Celso M. De Melo
D. Handelman
179
0
0
08 Oct 2025
Best-of-Both-Worlds Algorithms for Linear Contextual Bandits
Yuko Kuroki
Alberto Rumi
Taira Tsuchiya
Fabio Vitale
Nicolò Cesa-Bianchi
339
13
0
24 Dec 2023
Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits
Neural Information Processing Systems (NeurIPS), 2023
Haolin Liu
Chen-Yu Wei
Julian Zimmert
299
14
0
02 Sep 2023
Stability-penalty-adaptive follow-the-regularized-leader: Sparsity, game-dependency, and best-of-both-worlds
Neural Information Processing Systems (NeurIPS), 2023
Taira Tsuchiya
Shinji Ito
Junya Honda
329
14
0
26 May 2023
First- and Second-Order Bounds for Adversarial Linear Contextual Bandits
Neural Information Processing Systems (NeurIPS), 2023
Julia Olkhovskaya
J. Mayo
T. Erven
Gergely Neu
Chen-Yu Wei
382
17
0
01 May 2023
Best-of-three-worlds Analysis for Linear Bandits with Follow-the-regularized-leader Algorithm
Annual Conference Computational Learning Theory (COLT), 2023
Fang-yuan Kong
Canzhe Zhao
Shuai Li
287
15
0
13 Mar 2023
Improved Best-of-Both-Worlds Guarantees for Multi-Armed Bandits: FTRL with General Regularizers and Multiple Optimal Arms
Neural Information Processing Systems (NeurIPS), 2023
Tiancheng Jin
Junyan Liu
Haipeng Luo
AAML
302
21
0
27 Feb 2023
A Blackbox Approach to Best of Both Worlds in Bandits and Beyond
Annual Conference Computational Learning Theory (COLT), 2023
Christoph Dann
Chen-Yu Wei
Julian Zimmert
296
29
0
20 Feb 2023
Best of Both Worlds Policy Optimization
International Conference on Machine Learning (ICML), 2023
Christoph Dann
Chen-Yu Wei
Julian Zimmert
265
17
0
18 Feb 2023
Best-of-Both-Worlds Algorithms for Partial Monitoring
International Conference on Algorithmic Learning Theory (ALT), 2022
Taira Tsuchiya
Shinji Ito
Junya Honda
530
18
0
29 Jul 2022
Nearly Optimal Best-of-Both-Worlds Algorithms for Online Learning with Feedback Graphs
Neural Information Processing Systems (NeurIPS), 2022
Shinji Ito
Taira Tsuchiya
Junya Honda
295
28
0
02 Jun 2022
A Near-Optimal Best-of-Both-Worlds Algorithm for Online Learning with Feedback Graphs
Neural Information Processing Systems (NeurIPS), 2022
Chloé Rouyer
Dirk van der Hoeven
Nicolò Cesa-Bianchi
Yevgeny Seldin
253
18
0
01 Jun 2022
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions
Neural Information Processing Systems (NeurIPS), 2022
Jiafan He
Dongruo Zhou
Tong Zhang
Quanquan Gu
327
57
0
13 May 2022
Linear Contextual Bandits with Adversarial Corruptions
Heyang Zhao
Dongruo Zhou
Quanquan Gu
AAML
257
25
0
25 Oct 2021
Regret Lower Bound and Optimal Algorithm for High-Dimensional Contextual Linear Bandit
Electronic Journal of Statistics (EJS), 2021
Ke Li
Yun Yang
N. Narisetty
258
10
0
23 Sep 2021
Policy Optimization in Adversarial MDPs: Improved Exploration via Dilated Bonuses
Neural Information Processing Systems (NeurIPS), 2021
Haipeng Luo
Chen-Yu Wei
Chung-Wei Lee
318
52
0
18 Jul 2021
Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks
International Conference on Artificial Intelligence and Statistics (AISTATS), 2021
Qin Ding
Cho-Jui Hsieh
James Sharpnack
AAML
271
39
0
05 Jun 2021
Improved Analysis of the Tsallis-INF Algorithm in Stochastically Constrained Adversarial Bandits and Stochastic Bandits with Adversarial Corruptions
Annual Conference Computational Learning Theory (COLT), 2021
Saeed Masoudian
Yevgeny Seldin
245
18
0
23 Mar 2021
Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously
International Conference on Machine Learning (ICML), 2021
Chung-Wei Lee
Haipeng Luo
Chen-Yu Wei
Mengxiao Zhang
Xiaojin Zhang
281
53
0
11 Feb 2021
Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition
Neural Information Processing Systems (NeurIPS), 2020
Tiancheng Jin
Haipeng Luo
347
61
0
10 Jun 2020
Efficient and Robust Algorithms for Adversarial Linear Contextual Bandits
Annual Conference Computational Learning Theory (COLT), 2020
Gergely Neu
Julia Olkhovskaya
480
53
0
01 Feb 2020
Better Algorithms for Stochastic Bandits with Adversarial Corruptions
Anupam Gupta
Tomer Koren
Kunal Talwar
AAML
372
164
0
22 Feb 2019
Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously
Julian Zimmert
Haipeng Luo
Chen-Yu Wei
524
89
0
25 Jan 2019
Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits
Journal of machine learning research (JMLR), 2018
Julian Zimmert
Yevgeny Seldin
AAML
705
209
0
19 Jul 2018
Competitive caching with machine learned advice
Thodoris Lykouris
Sergei Vassilvitskii
352
467
0
15 Feb 2018
More Adaptive Algorithms for Adversarial Bandits
Chen-Yu Wei
Haipeng Luo
693
201
0
10 Jan 2018
An Improved Parametrization and Analysis of the EXP3++ Algorithm for Stochastic and Adversarial Bandits
Annual Conference Computational Learning Theory (COLT), 2017
Yevgeny Seldin
Gábor Lugosi
214
93
0
20 Feb 2017
Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits
Neural Information Processing Systems (NeurIPS), 2016
Vasilis Syrgkanis
Haipeng Luo
A. Krishnamurthy
Robert Schapire
284
43
0
01 Jun 2016
An algorithm with nearly optimal pseudo-regret for both stochastic and adversarial bandits
P. Auer
Chao-Kai Chiang
253
117
0
27 May 2016
Online Learning with Low Rank Experts
Elad Hazan
Tomer Koren
Roi Livni
Yishay Mansour
250
17
0
21 Mar 2016
BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits
Alexander Rakhlin
Karthik Sridharan
OffRL
571
72
0
06 Feb 2016
A Contextual-Bandit Approach to Personalized News Article Recommendation
The Web Conference (WWW), 2010
Lihong Li
Wei Chu
John Langford
Robert Schapire
1.1K
3,249
0
28 Feb 2010
Contextual Bandit Algorithms with Supervised Learning Guarantees
International Conference on Artificial Intelligence and Statistics (AISTATS), 2010
A. Beygelzimer
John Langford
Lihong Li
L. Reyzin
Robert Schapire
OffRL
582
346
0
22 Feb 2010
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