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lower-tail calibration

topic1 events
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.

bayesian-optimization/gaussian-processes/calibration/arxiv