Self-Correcting Bayesian Optimization through Bayesian Active Learning

arXiv:2304.11005v3 Announce Type: replace
Abstract: Gaussian processes are the model of choice in Bayesian optimization and active learning. Yet, they are highly dependent on cleverly chosen hyperparameters to reach their full potential, and little effort is devoted to finding good hyperparameters in the literature. We demonstrate the impact of selecting good hyperparameters for GPs and present two acquisition functions that explicitly prioritize hyperparameter learning. Statistical distance-based Active Learning (SAL) considers the average disagreement between samples from the posterior, as measured …

abstract active learning arxiv bayesian cs.lg gaussian processes good impact literature optimization processes stat.ml through type