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News/Near-Optimal Regret in Adversarial Kernel Bandits
arxiv
PublishedMay 27, 2026 at 4:00 AM
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Near-Optimal Regret in Adversarial Kernel Bandits

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arXiv:2605.26585v1 Announce Type: new Abstract: We study the adversarial kernel bandit problem, in which the loss at each round is induced by an arbitrary bounded element of a reproducing kernel Hilbert space (RKHS). We propose an exponential-weights algorithm built on a regularized importance-weigh

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