  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.
