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Improved Best-of-Both-Worlds Guarantees for Multi-Armed Bandits: FTRL
  with General Regularizers and Multiple Optimal Arms
v1v2 (latest)

Improved Best-of-Both-Worlds Guarantees for Multi-Armed Bandits: FTRL with General Regularizers and Multiple Optimal Arms

Neural Information Processing Systems (NeurIPS), 2023
27 February 2023
Tiancheng Jin
Junyan Liu
Haipeng Luo
    AAML
ArXiv (abs)PDFHTMLGithub

Papers citing "Improved Best-of-Both-Worlds Guarantees for Multi-Armed Bandits: FTRL with General Regularizers and Multiple Optimal Arms"

15 / 15 papers shown
Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality
Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality
Chaiwon Kim
Jongyeong Lee
Min-hwan Oh
143
0
0
14 Oct 2025
FLEET: Formal Language-Grounded Scheduling for Heterogeneous Robot Teams
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
Revisiting Follow-the-Perturbed-Leader with Unbounded Perturbations in Bandit Problems
Revisiting Follow-the-Perturbed-Leader with Unbounded Perturbations in Bandit Problems
Jongyeong Lee
Junya Honda
Shinji Ito
Min-hwan Oh
226
2
0
26 Aug 2025
A Near-optimal, Scalable and Parallelizable Framework for Stochastic Bandits Robust to Adversarial Corruptions and Beyond
A Near-optimal, Scalable and Parallelizable Framework for Stochastic Bandits Robust to Adversarial Corruptions and Beyond
Zicheng Hu
Cheng Chen
471
0
0
11 Feb 2025
uniINF: Best-of-Both-Worlds Algorithm for Parameter-Free Heavy-Tailed MABs
uniINF: Best-of-Both-Worlds Algorithm for Parameter-Free Heavy-Tailed MABsInternational Conference on Learning Representations (ICLR), 2024
Yu Chen
Jiatai Huang
Yan Dai
Longbo Huang
469
6
0
04 Oct 2024
Optimism in the Face of Ambiguity Principle for Multi-Armed Bandits
Optimism in the Face of Ambiguity Principle for Multi-Armed Bandits
Mengmeng Li
Daniel Kuhn
Bahar Taşkesen
526
2
0
30 Sep 2024
Bellman Diffusion Models
Bellman Diffusion Models
Liam Schramm
Abdeslam Boularias
DiffM
390
2
0
16 Jul 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(T2/3)Θ(T^{2/3})Θ(T2/3) and its Application to Best-of-Both-Worlds
Taira Tsuchiya
Shinji Ito
488
4
0
30 May 2024
LC-Tsallis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits
LC-Tsallis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits
Masahiro Kato
Shinji Ito
597
2
0
05 Mar 2024
Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive
  Analysis and Best-of-Both-Worlds
Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds
Shinji Ito
Taira Tsuchiya
Junya Honda
510
9
0
01 Mar 2024
Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret
  with Adversarial Robustness in Partial Monitoring
Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring
Taira Tsuchiya
Shinji Ito
Junya Honda
317
3
0
13 Feb 2024
Best-of-Both-Worlds Linear Contextual Bandits
Best-of-Both-Worlds Linear Contextual Bandits
Masahiro Kato
Shinji Ito
319
2
0
27 Dec 2023
Stability-penalty-adaptive follow-the-regularized-leader: Sparsity,
  game-dependency, and best-of-both-worlds
Stability-penalty-adaptive follow-the-regularized-leader: Sparsity, game-dependency, and best-of-both-worldsNeural Information Processing Systems (NeurIPS), 2023
Taira Tsuchiya
Shinji Ito
Junya Honda
330
14
0
26 May 2023
On the Minimax Regret for Online Learning with Feedback Graphs
On the Minimax Regret for Online Learning with Feedback GraphsNeural Information Processing Systems (NeurIPS), 2023
Khaled Eldowa
Emmanuel Esposito
Tommaso Cesari
Nicolò Cesa-Bianchi
256
8
0
24 May 2023
Best-of-Three-Worlds Linear Bandit Algorithm with Variance-Adaptive
  Regret Bounds
Best-of-Three-Worlds Linear Bandit Algorithm with Variance-Adaptive Regret BoundsAnnual Conference Computational Learning Theory (COLT), 2023
Shinji Ito
Kei Takemura
194
15
0
24 Feb 2023
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