用扩散模型优化离线黑箱设计，提升生成新方案的准确性和可靠性。
Support-Proximity Augmented Diffusion Estimation for Offline Black-Box Optimization
- 基于条件生成建模，用扩散模型预测属性值与设计的关系。
- 引入校准与邻近正则化，显著提升对未知设计的可信度评估。
- 适合需要从静态数据中挖掘高质量新设计的研究者使用。
离线黑箱优化旨在仅利用静态数据集发现具有高属性评分的新设计方案，但面临分布外（OOD）外推的根本性挑战。现有方法分为反向方法（难以映射评分到设计）和前向方法（缺乏有效量化不确定性的表达能力）。本文提出SPADE（支持-邻近增强扩散估计），通过条件生成建模重新构想前向代理建模。SPADE使用扩散模型建模前向似然p(y|x)，并引入两项关键改进：(1) 校准扩散估计模块，确保统计矩与成对排名的全局一致性；(2) 支持-邻近正则化机制，通过kNN密度估计隐式内化数据流形约束p(x)。理论上，该正则化等价于最大化具有有效设计先验的贝叶斯后验。实验表明，SPADE在Design-Bench任务和大型语言模型数据混合优化基准上均达到当前最优性能。
原文摘要 · Abstract (English)
Offline black-box optimization aims to discover novel designs with high property scores using only a static dataset, a task fundamentally challenged by the out-of-distribution (OOD) extrapolation problem. Existing approaches typically bifurcate into inverse methods, which struggle with the ill-posed nature of mapping scores to designs, and forward methods, which often lack the distributional expressivity to quantify uncertainty effectively. In this work, we propose SPADE (Support-Proximity Augmented Diffusion Estimation), a novel framework that reimagines forward surrogate modeling through the lens of conditional generative modeling. SPADE models the forward likelihood p(y|x) using a diffusion model, but with two critical enhancements to tailor it for optimization: (1) a Calibrated Diffusion Estimation module that enforces global consistency in statistical moments and pairwise rankings, and (2) a Support-Proximity Regularization mechanism that implicitly internalizes the data manifold constraint p(x) via kNN-based density estimation. Theoretically, we prove that our regularization is first-order equivalent to maximizing a Bayesian posterior with a valid design prior. Empirically, SPADE achieves state-of-the-art performance across Design-Bench tasks and an LLM data mixture optimization benchmark.
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