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Understanding Hidden Spatial Preferences in Bayesian Optimization

Bånkestad, Maria et al.
arXiv (All) · Papers
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machine-learning
Bayesian optimization uses posterior uncertainty to decide where to explore, but this uncertainty is not spatially neutral. On bounded domains, Gaussian-process posterior variance is typically larger near faces, edges, and corners, giving variance-based acquisition functions a systematic boundary bi
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