Self-Correcting Bayesian Optimization through Bayesian Active Learning. (arXiv:2304.11005v1 [cs.LG])

Gaussian processes are cemented as the model of choice in Bayesian
optimization and active learning. Yet, they are severely dependent on cleverly
chosen hyperparameters to reach their full potential, and little effort is
devoted to finding the right hyperparameters in the literature. We demonstrate
the impact of selecting good hyperparameters for GPs and present two
acquisition functions that explicitly prioritize this goal. Statistical
distance-based Active Learning (SAL) considers the average disagreement among
samples from the posterior, as measured by a …

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