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papersSEP 11 04:00 UTC

arXiv paper targets lower-tail calibration of Gaussian processes for Bayesian optimization

An updated arXiv preprint proposes a goal-oriented approach to calibrating the lower tail of Gaussian process predictive distributions, which Bayesian optimization uses to choose where to evaluate costly objective functions. The abstract notes that kernel and hyperparameter choices strongly shape these predictions. The submission is a replacement version (v2) of an earlier preprint.

arXiv Gaussian process bayesian-optimization hyperparameter-tuning kernel-methods lower-tail calibration

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arXiv cs.LGGoal-Oriented Lower-Tail Calibration of Gaussian Processes for Bayesian Optimization ↗SEP 11 04:00 UTC