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.

离线基于模型的优化（MBO）旨在最大化黑箱目标函数，仅使用设计和分数的离线数据集。我们引入一种名为离线模型优化设计编辑（DEMO）的新方法，通过在离线数据集上应用训练好的代理模型的梯度上升来产生一个合成数据集，用于训练条件扩散模型以生成高分设计，并通过直接将离线数据集中的高分特征加入到设计生成过程中进行了改进。实证评估表明，DEMO 在 7 个离线 MBO 任务中优于各种基准方法。