Cover Image

On the Benefits of Free Exploration for Regret Minimization in Multi-Armed Bandits

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Hou, Yunlong, Zhong, Zixin, Tan, Vincent Y. F.
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence Information Theory
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Almost Minimax Optimal Best Arm Identification in Piecewise Stationary Linear Bandits
by: Hou, Yunlong, et al.
Published: (2024)

Asymptotically and Minimax Optimal Regret Bounds for Multi-Armed Bandits with Abstention
by: Yang, Junwen, et al.
Published: (2024)

Optimal Clustering with Bandit Feedback
by: Yang, Junwen, et al.
Published: (2022)

Near-Optimal Regret for KL-Regularized Multi-Armed Bandits
by: Ji, Kaixuan, et al.
Published: (2026)

Best Arm Identification with Minimal Regret
by: Yang, Junwen, et al.
Published: (2024)

Asymptotically Optimal Linear Best Feasible Arm Identification with Fixed Budget
by: Bian, Jie, et al.
Published: (2025)

Optimal Multi-Objective Best Arm Identification with Fixed Confidence
by: Chen, Zhirui, et al.
Published: (2025)

On the Exponential Convergence for Offline RLHF with Pairwise Comparisons
by: Chen, Zhirui, et al.
Published: (2024)

Best Arm Identification with Possibly Biased Offline Data
by: Yang, Le, et al.
Published: (2025)

Diminishing Exploration: A Minimalist Approach to Piecewise Stationary Multi-Armed Bandits
by: Li, Kuan-Ta, et al.
Published: (2024)

Influence Maximization via Graph Neural Bandits
by: Feng, Yuting, et al.
Published: (2024)

Combinatorial Multi-armed Bandits: Arm Selection via Group Testing
by: Mukherjee, Arpan, et al.
Published: (2024)

On the Optimal Sample Complexity of Offline Multi-Armed Bandits with KL Regularization
by: Ji, Kaixuan, et al.
Published: (2026)

Fixed-Budget Differentially Private Best Arm Identification
by: Chen, Zhirui, et al.
Published: (2024)

Regret Bounds for Noise-Free Cascaded Kernelized Bandits
by: Li, Zihan, et al.
Published: (2022)

Quantile Multi-Armed Bandits with 1-bit Feedback
by: Lau, Ivan, et al.
Published: (2025)

Indexed Minimum Empirical Divergence-Based Algorithms for Linear Bandits
by: Bian, Jie, et al.
Published: (2024)

Bandit Convex Optimization with Gradient Prediction Adaptivity
by: Wang, Shuche, et al.
Published: (2026)

Flickering Multi-Armed Bandits
by: Chakraborty, Sourav, et al.
Published: (2026)

BanditSpec: Adaptive Speculative Decoding via Bandit Algorithms
by: Hou, Yunlong, et al.
Published: (2025)

ArcMark: Distortion-Free Multi-Byte LLM Watermark via Optimal Transport
by: Gilani, Atefeh, et al.
Published: (2026)

Remote Action Generation: Remote Control with Minimal Communication
by: Kobus, Szymon, et al.
Published: (2026)

Multi-Armed Bandits With Best-Action Queries
by: Bacchiocchi, Francesco, et al.
Published: (2026)

Fast Rates for Offline Contextual Bandits with Forward-KL Regularization under Single-Policy Concentrability
by: Zhao, Qingyue, et al.
Published: (2026)

Energy Saving for Cell-Free Massive MIMO Networks: A Multi-Agent Deep Reinforcement Learning Approach
by: Wang, Qichen, et al.
Published: (2026)

Evolution of Information in Interactive Decision Making: A Case Study for Multi-Armed Bandits
by: Gu, Yuzhou, et al.
Published: (2025)

Optimal Best Arm Identification with Fixed Confidence in Restless Bandits
by: Karthik, P. N., et al.
Published: (2023)

$(ε, u)$-Adaptive Regret Minimization in Heavy-Tailed Bandits
by: Genalti, Gianmarco, et al.
Published: (2023)

Quantum-Enhanced Neural Contextual Bandit Algorithms
by: Huang, Yuqi, et al.
Published: (2026)

Introduction to Multi-Armed Bandits
by: Slivkins, Aleksandrs
Published: (2019)

Fair Algorithms with Probing for Multi-Agent Multi-Armed Bandits
by: Xu, Tianyi, et al.
Published: (2025)

Large Language Model-Enhanced Multi-Armed Bandits
by: Sun, Jiahang, et al.
Published: (2025)

Multi-Armed Bandits-Based Optimization of Decision Trees
by: Shanto, Hasibul Karim, et al.
Published: (2025)

Intrinsic Fairness-Accuracy Tradeoffs under Equalized Odds
by: Zhong, Meiyu, et al.
Published: (2024)

A Frequency-Domain Analysis of the Multi-Armed Bandit Problem: A New Perspective on the Exploration-Exploitation Trade-off
by: Zhang, Di
Published: (2025)

p-Mean Regret for Stochastic Bandits
by: Krishna, Anand, et al.
Published: (2024)

Regret Tail Characterization of Optimal Bandit Algorithms with Generic Rewards
by: Panda, Subhodip, et al.
Published: (2026)

Improved Regret Bounds for Linear Bandits with Heavy-Tailed Rewards
by: Tajdini, Artin, et al.
Published: (2025)

Next-Token Prediction and Regret Minimization
by: Mohri, Mehryar, et al.
Published: (2026)

Global Rewards in Restless Multi-Armed Bandits
by: Raman, Naveen, et al.
Published: (2024)