A General (sqrt T gamma T) Lower Bound for Kernel Bandits

arXiv:2610.11082 · stat.ML, cs.IT, cs.LG, math.IT · Submitted 2026-10-08 · Read on arXiv

stat.ML, cs.IT, cs.LG, math.IT

Submitted: 2026-10-08

Updated: 2026-10-08

Terminology

• Regret — 49× in this paper · explained in 3 episodes
• Information Gain — 22× in this paper · explained in 2 episodes
• Minimax Regret — 13× in this paper · explained in 2 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Hilbert Space — 2× in this paper · explained in 2 episodes
• Reproducing Kernel Hilbert Space — 2× in this paper
• Reproducing Kernel Hilbert Space (RKHS) — 2× in this paper · explained in 3 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Bayesian Optimization — 1× in this paper · explained in 3 episodes
• Contextual Bandits — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes

Sources

• Consequences of Kernel Regularity for Bandit Optimization
• Batched Kernelized Bandits: Refinements and Extensions

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