  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.

Machine Learning