Active Learning for Enumerating Local Minima Based on Gaussian Process Derivatives
Active Learning for Enumerating Local Minima Based on Gaussian Process Derivatives
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DOI：
10.1162/neco_a_01307
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发表时间：
2019-03
影响因子：
2.9
通讯作者：
Yu Inatsu;Daisuke Sugita;K. Toyoura;I. Takeuchi
中科院分区：
文献类型：
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作者：
Yu Inatsu;Daisuke Sugita;K. Toyoura;I. Takeuchi
我们研究主动学习（AL）的高斯过程（GP）的基础上，有效地枚举所有的黑盒函数的局部最小解。这个问题是具有挑战性的，因为局部解的特征在于它们的零梯度和正定海森性质，但这些导数不能直接观察到。我们提出了一种新的AL方法，在该方法中，输入点被顺序选择，从而有效地更新GP导数的置信区间，以枚举局部最小解。我们从理论上分析了所提出的方法，并通过数值实验证明了其有效性。
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.