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1802.03386
Cited By
Make the Minority Great Again: First-Order Regret Bound for Contextual Bandits
9 February 2018
Zeyuan Allen-Zhu
Sébastien Bubeck
Yuanzhi Li
LRM
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Papers citing
"Make the Minority Great Again: First-Order Regret Bound for Contextual Bandits"
26 / 26 papers shown
Data-Dependent Regret Bounds for Constrained MABs
Gianmarco Genalti
Francesco Emanuele Stradi
Matteo Castiglioni
A. Marchesi
N. Gatti
371
0
0
26 May 2025
How Does Variance Shape the Regret in Contextual Bandits?
Neural Information Processing Systems (NeurIPS), 2024
Zeyu Jia
Jian Qian
Alexander Rakhlin
Chen-Yu Wei
403
9
0
16 Oct 2024
Improved Regret Bounds for Bandits with Expert Advice
Nicolò Cesa-Bianchi
Khaled Eldowa
Emmanuel Esposito
Julia Olkhovskaya
181
2
0
24 Jun 2024
Optimistic Information Directed Sampling
Gergely Neu
Matteo Papini
Ludovic Schwartz
293
3
0
23 Feb 2024
Information Capacity Regret Bounds for Bandits with Mediator Feedback
Khaled Eldowa
Nicolò Cesa-Bianchi
Alberto Maria Metelli
Marcello Restelli
199
3
0
15 Feb 2024
Best-of-Both-Worlds Algorithms for Linear Contextual Bandits
Yuko Kuroki
Alberto Rumi
Taira Tsuchiya
Fabio Vitale
Nicolò Cesa-Bianchi
283
11
0
24 Dec 2023
Settling the Sample Complexity of Online Reinforcement Learning
Annual Conference Computational Learning Theory (COLT), 2023
Zihan Zhang
Yuxin Chen
Jason D. Lee
S. Du
OffRL
710
34
0
25 Jul 2023
The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning
Neural Information Processing Systems (NeurIPS), 2023
Kaiwen Wang
Kevin Zhou
Runzhe Wu
Nathan Kallus
Wen Sun
OffRL
458
23
0
25 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
261
13
0
01 May 2023
Adaptivity and Non-stationarity: Problem-dependent Dynamic Regret for Online Convex Optimization
Journal of machine learning research (JMLR), 2021
Peng Zhao
Yu Zhang
Lijun Zhang
Zhi Zhou
337
77
0
29 Dec 2021
First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach
International Conference on Machine Learning (ICML), 2021
Andrew Wagenmaker
Yifang Chen
Max Simchowitz
S. Du
Kevin Jamieson
315
47
0
07 Dec 2021
Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination
Dylan J. Foster
A. Krishnamurthy
183
53
0
05 Jul 2021
Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations
Annual Conference Computational Learning Theory (COLT), 2020
Yossi Arjevani
Y. Carmon
John C. Duchi
Dylan J. Foster
Ayush Sekhari
Karthik Sridharan
292
63
0
24 Jun 2020
Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs
Neural Information Processing Systems (NeurIPS), 2020
Chung-Wei Lee
Haipeng Luo
Chen-Yu Wei
Mengxiao Zhang
344
59
0
14 Jun 2020
Bandits with adversarial scaling
International Conference on Machine Learning (ICML), 2020
Thodoris Lykouris
Vahab Mirrokni
R. Leme
168
14
0
04 Mar 2020
Taking a hint: How to leverage loss predictors in contextual bandits?
Annual Conference Computational Learning Theory (COLT), 2020
Chen-Yu Wei
Haipeng Luo
Alekh Agarwal
304
28
0
04 Mar 2020
Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles
International Conference on Machine Learning (ICML), 2020
Dylan J. Foster
Alexander Rakhlin
572
225
0
12 Feb 2020
A Closer Look at Small-loss Bounds for Bandits with Graph Feedback
Annual Conference Computational Learning Theory (COLT), 2020
Chung-Wei Lee
Haipeng Luo
Mengxiao Zhang
192
24
0
02 Feb 2020
Efficient and Robust Algorithms for Adversarial Linear Contextual Bandits
Annual Conference Computational Learning Theory (COLT), 2020
Gergely Neu
Julia Olkhovskaya
371
52
0
01 Feb 2020
Model selection for contextual bandits
Neural Information Processing Systems (NeurIPS), 2019
Dylan J. Foster
A. Krishnamurthy
Haipeng Luo
OffRL
519
96
0
03 Jun 2019
First-Order Bayesian Regret Analysis of Thompson Sampling
IEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2019
Sébastien Bubeck
Mark Sellke
320
19
0
02 Feb 2019
Improved Path-length Regret Bounds for Bandits
Annual Conference Computational Learning Theory (COLT), 2019
Sébastien Bubeck
Yuanzhi Li
Haipeng Luo
Chen-Yu Wei
285
47
0
29 Jan 2019
Fighting Contextual Bandits with Stochastic Smoothing
Young Hun Jung
Ambuj Tewari
AAML
101
0
0
11 Oct 2018
A Contextual Bandit Bake-off
A. Bietti
Alekh Agarwal
John Langford
744
115
0
12 Feb 2018
Online Learning via the Differential Privacy Lens
Jacob D. Abernethy
Young Hun Jung
Chansoo Lee
Audra McMillan
Ambuj Tewari
252
13
0
27 Nov 2017
Small-loss bounds for online learning with partial information
Thodoris Lykouris
Karthik Sridharan
Éva Tardos
271
41
0
09 Nov 2017
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