Diffusion Models for Black-Box Optimization

• URL: http://arxiv.org/abs/2306.07180v2
• Date: Mon, 21 Aug 2023 16:55:58 GMT
• Title: Diffusion Models for Black-Box Optimization
• Authors: Siddarth Krishnamoorthy, Satvik Mehul Mashkaria, Aditya Grover
• Abstract summary: We propose Denoising Diffusion Optimization Models (DDOM) for offline black-box optimization. Given an offline dataset, DDOM learns a conditional generative model over the domain of the black-box function conditioned on the function values. We show DDOM achieves results competitive with state-of-the-art baselines.
• Score: 29.64357898080842
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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