AI RESEARCH
BOOST: A Data-Driven Framework for the Automated Joint Selection of Kernel and Acquisition Functions in Bayesian Optimization
arXiv CS.LG
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ArXi:2508.02332v3 Announce Type: replace The performance of Bayesian optimization (BO), a highly sample-efficient method for expensive black-box problems, is critically governed by the selection of its hyperparameters, including the kernel and acquisition functions. This presents a significant practical challenge: an inappropriate combination of these can lead to poor performance and wasted evaluations.