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6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
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2.21

Neural Computation

October 2020, Vol. 32, No. 10, Pages 2032-2068
(doi: 10.1162/neco_a_01307)
© 2020 Massachusetts Institute of Technology
Active Learning for Enumerating Local Minima Based on Gaussian Process Derivatives
Article PDF (2.71 MB)
Abstract
We study active learning (AL) based on gaussian processes (GPs) for efficiently enumerating all of the local minimum solutions of a black-box function. This problem is challenging because local solutions are characterized by their zero gradient and positive-definite Hessian properties, but those derivatives cannot be directly observed. We propose a new AL method in which the input points are sequentially selected such that the confidence intervals of the GP derivatives are effectively updated for enumerating local minimum solutions. We theoretically analyze the proposed method and demonstrate its usefulness through numerical experiments.