Design Editing for Offline Model-based Optimization

Offline model-based optimization (MBO) aims to maximize a black-box objective function using only an offline dataset of designs and scores. A prevalent approach involves training a conditional generative model on existing designs and their associated scores, followed by the generation of new designs conditioned on higher target scores. However, these newly generated designs often underperform due to the lack of high-scoring training data. To address this challenge, we introduce a novel method, Design Editing for Offline Model-based Optimization (DEMO), which consists of two phases. In the first phase, termed pseudo-target distribution generation, we apply gradient ascent on the offline dataset using a trained surrogate model, producing a synthetic dataset where the predicted scores serve as new labels. A conditional diffusion model is subsequently trained on this synthetic dataset to capture a pseudo-target distribution, which enhances the accuracy of the conditional diffusion model in generating higher-scoring designs. Nevertheless, the pseudo-target distribution is susceptible to noise stemming from inaccuracies in the surrogate model, consequently predisposing the conditional diffusion model to generate suboptimal designs. We hence propose the second phase, existing design editing, to directly incorporate the high-scoring features from the offline dataset into design generation. In this phase, top designs from the offline dataset are edited by introducing noise, which are subsequently refined using the conditional diffusion model to produce high-scoring designs. Overall, high-scoring designs begin with inheriting high-scoring features from the second phase and are further refined with a more accurate conditional diffusion model in the first phase. Empirical evaluations on 7 offline MBO tasks show that DEMO outperforms various baseline methods.