AI RESEARCH
Nearly-Optimal Algorithm for Adversarial Kernelized Bandits
arXiv CS.LG
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ArXi:2605.10299v1 Announce Type: new This paper studies kernelized bandits (also known as Gaussian process bandits) in an adversarial environment, where the reward functions in a known reproducing kernel Hilbert space (RKHS) may be adversarially chosen at each round. We show that the exponential-weight algorithm achieves $\tilde{O}(\sqrt{T \gamma_T})$ adversarial regret, where $T$ and $\gamma_T$ denote the number of total rounds and the maximum information gain, respectively.