Correcting boundary over-exploration deficiencies in Bayesian optimization with virtual derivative sign observations

4 Apr 2017Eero SiivolaAki VehtariJarno VanhataloJavier GonzálezMichael Riis Andersen

Bayesian optimization (BO) is a global optimization strategy designed to find the minimum of an expensive black-box function, typically defined on a compact subset of $\mathcal{R}^d$, by using a Gaussian process (GP) as a surrogate model for the objective. Although currently available acquisition functions address this goal with different degree of success, an over-exploration effect of the contour of the search space is typically observed... (read more)

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