A stopping criterion for Bayesian optimization by the gap of expected minimum simple regrets
A stopping criterion for Bayesian optimization by the gap of expected minimum simple regrets
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发表时间：
2023
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通讯作者：
Hideaki Ishibashi;Masayuki Karasuyama;I. Takeuchi;H. Hino
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作者：
Hideaki Ishibashi;Masayuki Karasuyama;I. Takeuchi;H. Hino
贝叶斯优化（BO）提高了黑盒优化的效率;然而，相关的计算成本和功耗在机器学习方法的应用中仍然占主导地位。本文提出了一种确定BO停时的方法。建议的标准是基于一个简单的遗憾之前和之后评估的目标函数与一个新的参数设置的一个变量的最小值的期望之间的差异。与现有的停止标准，建议的标准是保证收敛到理论上的最优停止标准的任意选择的采集功能和阈值。此外，停止准则的阈值可以自动和自适应地确定。我们通过实验证明，所提出的停止标准找到了合理的时间来停止BO，并对目标函数进行了少量的评估。
Bayesian optimization (BO) improves the effi-ciency of black-box optimization; however, the associated computational cost and power consumption remain dominant in the application of machine learning methods. This paper proposes a method of determining the stopping time in BO. The proposed criterion is based on the difference between the expectation of the minimum of a variant of the simple regrets before and after evaluating the objective function with a new parameter setting. Unlike existing stopping criteria, the proposed criterion is guaranteed to converge to the theoretically optimal stopping criterion for any choices of arbitrary acquisition functions and threshold values. Moreover, the threshold for the stopping criterion can be determined automatically and adaptively. We experimentally demonstrate that the proposed stopping criterion finds reasonable timing to stop a BO with a small number of evaluations of the objective function.