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Goal-Oriented Lower-Tail Calibration of Gaussian Processes for Bayesian Optimization
Gaussian process (GP) predictive distributions are commonly used in Bayesian optimization (BO) to guide the selection of evaluation points for expensive objective functions. The choice of kernel and hyperparameters has a strong influence on the exploration--exploitation trade-off. For minimization,
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