The goal of offline black-box optimization (BBO) is to optimize an expensive black-box function using a fixed dataset of function evaluations. Prior works consider forward approaches that learn surrogates to the black-box function and inverse approaches that directly map function values to corresponding points in the input domain of the black-box function. These approaches are limited by the quality of the offline dataset and the difficulty in learning one-to-many mappings in high dimensions, respectively. We propose Denoising Diffusion Optimization Models (DDOM), a new inverse approach for offline black-box optimization based on diffusion models. Given an offline dataset, DDOM learns a conditional generative model over the domain of the black-box function conditioned on the function values. We investigate several design choices in DDOM, such as re-weighting the dataset to focus on high function values and the use of classifier-free guidance at test-time to enable generalization to function values that can even exceed the dataset maxima. Empirically, we conduct experiments on the Design-Bench benchmark and show that DDOM achieves results competitive with state-of-the-art baselines.

翻译：离线黑箱优化（Black-Box Optimization, BBO）的目标是利用固定的函数评估数据集优化一个昂贵的黑箱函数。现有工作包括前向方法（学习黑箱函数的替代模型）和逆方法（直接将函数值映射到黑箱函数输入域中的对应点）。前者受限于离线数据集的质量，后者则存在高维空间中学到一对多映射的困难。我们提出去噪扩散优化模型（Denoising Diffusion Optimization Models, DDOM），一种基于扩散模型的离线黑箱优化逆方法。给定离线数据集，DDOM学习一个以函数值为条件的黑箱函数域上的条件生成模型。我们研究了DDOM中的若干设计选择，例如对数据集进行重加权以聚焦高函数值，以及在测试时使用无分类器引导以实现对甚至超过数据集最大值的函数值的泛化。通过Design-Bench基准的实验表明，DDOM取得了与最先进基线方法相竞争的结果。