Bandits with Multiple Optimal Arms: Minimax Regret and Non-Adaptivity

arXiv:2609.38659 · stat.ML, cs.AI, cs.LG, math.ST, stat.ME, stat.TH · Submitted 2026-09-29 · Read on arXiv

stat.ML, cs.AI, cs.LG, math.ST, stat.ME, stat.TH

Submitted: 2026-09-29

Updated: 2026-10-01

Terminology

• Regret — 113× in this paper · explained in 3 episodes
• Adaptivity — 12× in this paper · explained in 1 episode
• Minimax Regret — 6× in this paper · explained in 2 episodes
• Adaptation — 5× in this paper · explained in 1 episode
• Contextual Bandits — 4× in this paper · explained in 3 episodes
• Reward Function — 4× in this paper · explained in 2 episodes
• Minimax optimality — 3× in this paper · explained in 2 episodes
• Sample Complexity — 3× in this paper · explained in 15 episodes
• Core — 2× in this paper · explained in 2 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Abundance — 1× in this paper · explained in 1 episode
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• KL Regularization — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• On the Optimal Sample Complexity of Offline Multi-Armed Bandits with KL Regularization

Related papers

• Behavior of prediction performance metrics with rare events
• Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs
• A Posterior-Dynamics Framework for Imaging Inverse Problems with Pretrained Diffusion Priors
• One Permutation Is All You Need: Fast, Deterministic Feature Importance and Model Stress-Testing
• Online Conformal Prediction for Non-Exchangeable Panel Data
• Deep Time-Series Forecasting in 10 Years: A Survey