Maximizing acquisition functions for Bayesian optimization

NeurIPS 2018 James T. WilsonFrank HutterMarc Peter Deisenroth

Bayesian optimization is a sample-efficient approach to global optimization that relies on theoretically motivated value heuristics (acquisition functions) to guide its search process. Fully maximizing acquisition functions produces the Bayes' decision rule, but this ideal is difficult to achieve since these functions are frequently non-trivial to optimize... (read more)

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