June 7, 2024, 4:43 a.m. | Natalie Maus, Kyurae Kim, Geoff Pleiss, David Eriksson, John P. Cunningham, Jacob R. Gardner

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.04308v1 Announce Type: new
Abstract: High-dimensional Bayesian optimization (BO) tasks such as molecular design often require 10,000 function evaluations before obtaining meaningful results. While methods like sparse variational Gaussian processes (SVGPs) reduce computational requirements in these settings, the underlying approximations result in suboptimal data acquisitions that slow the progress of optimization. In this paper we modify SVGPs to better align with the goals of BO: targeting informed data acquisition rather than global posterior fidelity. Using the framework of utility-calibrated variational …

abstract acquisitions approximation arxiv bayesian computational cs.lg data design function gaussian processes optimization paper processes progress reduce requirements results stat.ml tasks type while

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