# Black-Box Optimization with Local Generative Surrogates

arXiv: https://arxiv.org/abs/2002.04632 · HTML: https://mlsanity.com/paper/2002.04632/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2020.

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

- [Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning](https://mlsanity.com/paper/2209.07559/) (2022)
- [Advances in Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider](https://mlsanity.com/paper/2105.07530/) (2021)
- [Learning Instance-Specific Parameters of Black-Box Models Using Differentiable Surrogates](https://mlsanity.com/paper/2407.17530/) (2024)

## Abstract (arXiv)

We propose a novel method for gradient-based optimization of black-box simulators using differentiable local surrogate models. In fields such as physics and engineering, many processes are modeled with non-differentiable simulators with intractable likelihoods. Optimization of these forward models is particularly challenging, especially when the simulator is stochastic. To address such cases, we introduce the use of deep generative models to iteratively approximate the simulator in local neighborhoods of the parameter space. We demonstrate that these local surrogates can be used to approximate the gradient of the simulator, and thus enable gradient-based optimization of simulator parameters. In cases where the dependence of the simulator on the parameter space is constrained to a low dimensional submanifold, we observe that our method attains minima faster than baseline methods, including Bayesian optimization, numerical optimization, and approaches using score function gradient estimators.