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arXiv Machine Learning By Kavin Aravindan, Mani Tej Sriram, Gautam Dasarathy, Tejas Bodas

Online Adaptive Kernel Mixing for Gaussian Process Decision Making

Read the original on arXiv Machine Learning →

The paper introduces HACK GPs, a method that treats kernel selection for Gaussian Processes as an online learning problem with expert advice. Each candidate kernel is viewed as a GP expert, and a distribution over these experts is updated online using AdaHedge based on a loss that reflects both function fit and task alignment. Two variants—Mixture of Gaussians and categorical sampling—are presented, with theoretical guarantees that the weight concentrates on the best kernel under a loss‑gap condition, and empirical results show robust performance across Bayesian optimization, level set estimation, and Bayesian active learning compared to standard kernels and simple ensembles.

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