Regret Bounds for Noise-Free Kernel-Based Bandits

Sattar Vakili

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arXiv · 2002.05096

Regret Bounds for Noise-Free Kernel-Based Bandits

Sattar Vakili

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Abstract

Kernel-based bandit is an extensively studied black-box optimization problem, in which the objective function is assumed to live in a known reproducing kernel Hilbert space. While nearly optimal regret bounds (up to logarithmic factors) are established in the noisy setting, surprisingly, less is known about the noise-free setting (when the exact values of the underlying function is accessible without observation noise). We discuss several upper bounds on regret; none of which seem order optimal, and provide a conjecture on the order optimal regret bound.

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Sattar Vakili. 2020-02-12. Regret Bounds for Noise-Free Kernel-Based Bandits. https://arxiv.org/abs/2002.05096

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