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

arXiv: https://arxiv.org/abs/1703.06925 · HTML: https://mlsanity.com/paper/1703.06925/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2017.

## Built on by (most cited first, 3 of 3)

- [Minibatch Stochastic Three Points Method for Unconstrained Smooth Minimization](https://mlsanity.com/paper/2209.07883/) (2022)
- [High Probability Complexity Bounds of Trust-Region Stochastic Sequential Quadratic Programming with Heavy-Tailed Noise](https://mlsanity.com/paper/2503.19091/) (2025)
- [Adaptive Optimization for Prediction with Missing Data](https://mlsanity.com/paper/2402.01543/) (2024)

## Abstract (arXiv)

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.