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When approximating a black-box function, sampling with active learningfocussing on regions with non-linear responses tends to improve accuracy. Wepresent the FLOLA-Voronoi method introduced previously for deterministicresponses, and theoretically derive the impact of output uncertainty. Thealgorithm automatically puts more emphasis on exploration to provide moreinformation to the models.

Active Learning for Approximation of Expensive Functions with Normal Distributed Output Uncertainty