Bayesian optimization uses Gaussian process regression to build a surrogate model and acquisition functions to guide sampling for optimizing costly objective functions, including a new formal generalization of expected improvement to noisy evaluations.
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A Tutorial on Bayesian Optimization
Bayesian optimization uses Gaussian process regression to build a surrogate model and acquisition functions to guide sampling for optimizing costly objective functions, including a new formal generalization of expected improvement to noisy evaluations.