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arXiv · 1703.06925

Black-Box Optimization in Machine Learning with Trust Region Based Derivative Free Algorithm

Hiva Ghanbari·Katya Scheinberg
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Abstract

In this work, we utilize a Trust Region based Derivative Free Optimization (DFO-TR) method to directly maximize the Area Under Receiver Operating Characteristic Curve (AUC), which is a nonsmooth, noisy function. We show that AUC is a smooth function, in expectation, if the distributions of the positive and negative data points obey a jointly normal distribution. The practical performance of this algorithm is compared to three prominent Bayesian optimization methods and random search. The presented numerical results show that DFO-TR surpasses Bayesian optimization and random search on various black-box optimization problem, such as maximizing AUC and hyperparameter tuning.

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Hiva Ghanbari, Katya Scheinberg. 2017-03-20. Black-Box Optimization in Machine Learning with Trust Region Based Derivative Free Algorithm. https://arxiv.org/abs/1703.06925

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