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In this work, we utilize a Trust Region based Derivative Free Optimization(DFO-TR) method to directly maximize the Area Under Receiver OperatingCharacteristic Curve (AUC), which is a nonsmooth, noisy function. We show thatAUC is a smooth function, in expectation, if the distributions of the positiveand negative data points obey a jointly normal distribution. The practicalperformance of this algorithm is compared to three prominent Bayesianoptimization methods and random search. The presented numerical results showthat DFO-TR surpasses Bayesian optimization and random search on variousblack-box optimization problem, such as maximizing AUC and hyperparametertuning.

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