We propose a new uncertainty estimator for gradient-free optimisation of black-box simulators using deep generative surrogate models. Optimisation of these simulators is especially challenging for stochastic simulators and higher dimensions. To address these issues, we utilise a deep generative surrogate approach to model the black box response for the entire parameter space. We then leverage this knowledge to estimate the proposed uncertainty based on the Wasserstein distance - the Wasserstein uncertainty. This approach is employed in a posterior agnostic gradient-free optimisation algorithm that minimises regret over the entire parameter space. A series of tests were conducted to demonstrate that our method is more robust to the shape of both the black box function and the stochastic response of the black box than state-of-the-art methods, such as efficient global optimisation with a deep Gaussian process surrogate.

翻译：我们提出了一种新的不确定性估计器，用于通过深度生成代理模型对黑盒模拟器进行无梯度优化。这些模拟器的优化对于随机模拟器和高维问题尤其具有挑战性。为解决这些问题，我们采用深度生成代理方法对整个参数空间的黑盒响应进行建模。随后，我们利用这一知识，基于Wasserstein距离（即Wasserstein不确定性）来估计所提出的不确定性。该方法被应用于一种后验无关的无梯度优化算法中，该算法旨在最小化整个参数空间上的遗憾值。一系列测试表明，相较于深度高斯过程代理的高效全局优化等先进方法，我们的方法对黑盒函数形态及黑盒随机响应的变化具有更强的鲁棒性。