Provable Anytime Ensemble Sampling Algorithms in Nonlinear Contextual Bandits

arXiv:2510.10730 · cs.LG, cs.AI, stat.ML · Submitted 2025-10-12 · Read on arXiv

cs.LG, cs.AI, stat.ML

Submitted: 2025-10-12

Updated: 2026-09-22

Comments: 69 pages, 5 figures, 6 tables

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Regret — 130× in this paper · explained in 3 episodes
• Contextual Bandits — 32× in this paper · explained in 3 episodes
• Regularization — 21× in this paper · explained in 3 episodes
• Thompson Sampling — 12× in this paper
• Loss Function — 11× in this paper · explained in 2 episodes
• Reinforcement Learning — 7× in this paper · explained in 4 episodes
• Activation Function — 2× in this paper · explained in 1 episode
• Adaptivity — 2× in this paper · explained in 1 episode
• Core — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Theoretical Guarantees — 2× in this paper · explained in 2 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Langevin Dynamics — 1× in this paper · explained in 2 episodes
• Neural Bandit with Perturbed Reward — 1× in this paper
• Neural Network Approximation — 1× in this paper · explained in 2 episodes
• Reward Function — 1× in this paper · explained in 2 episodes
• Upper Confidence Bound — 1× in this paper

Sources

• What Doubling Tricks Can and Can't Do for Multi-Armed Bandits
• Generalized Linear Bandits: Almost Optimal Regret with One-Pass Update

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