Active learning for enumerating local minima based on Gaussian process derivatives

Autor:	Inatsu, Yu, Sugita, Daisuke, Toyoura, Kazuaki, Takeuchi, Ichiro
Rok vydání:	2019
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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 due to the fact that 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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1903.03279 Zobrazit plný text záznamu View this record from Arxiv