论文标题
局部生成替代物的黑盒优化
Black-Box Optimization with Local Generative Surrogates
论文作者
论文摘要
我们提出了一种新的方法，用于使用可区分的局部替代模型对黑框模拟器的优化。在物理和工程等领域中，许多过程都用具有棘手的可能性的非差异模拟器建模。这些正向模型的优化特别具有挑战性，尤其是在模拟器随机时。为了解决此类情况，我们将深层生成模型的使用介绍在参数空间的本地社区中迭代近似模拟器。我们证明这些局部替代物可用于近似模拟器的梯度，从而实现基于梯度的模拟器参数的优化。如果模拟器对参数空间的依赖性约束至较低的尺寸子序列，我们观察到，我们的方法比基线方法更快地达到最小值，包括贝叶斯优化，数值优化以及使用分数函数梯度估计器的方法。
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.
