让扩散模型生成含缺失值的表格数据，效果优于专门设计的模型。
AugMask: Training Diffusion Models on Incomplete Tabular Data via Stochastic Augmentation and Masking
- 用随机增强和掩码分离条件与监督信号
- 在多种缺失模式下超越专用基线模型
- 适合处理真实世界不完整表格数据的研究者
基于得分的扩散模型虽是主流生成模型，但应用于表格数据时面临挑战——其基础结构要求输入完整，而现实数据常含缺失值。我们提出 AugMask，一种即插即用的训练框架，通过将条件与监督分离，使无需缺失感知的骨干模型能适应不完整数据。AugMask 1）利用轻量辅助模型进行条件性随机增强构造数值输入；2）仅对观测到的坐标施加去噪监督。本质上，增强后的缺失项作为不确定的条件上下文，而非训练目标。我们将其训练规则与 Rao--Blackwellized 目标关联，证明边际化缺失项会带来方差加权的敏感度惩罚，抑制对不确定补全的过度依赖。在多种数据集和缺失模式下，AugMask 使标准扩散型表格生成器性能超过专用的缺失感知基线模型。
原文摘要 · Abstract (English)
Score-based diffusion models have emerged as prominent deep generative models; however, their application to tabular data remains challenging because their backbones assume fully specified inputs, whereas real-world tabular data often contain missing values. We propose AugMask, a plug-and-play training framework that adapts missing-unaware backbones to incomplete data by separating conditioning from supervision. AugMask 1) constructs numeric inputs via conditional stochastic augmentation using lightweight auxiliary models, and 2) applies denoising supervision only to observed coordinates. In effect, augmented missing entries serve as uncertain conditioning context rather than training targets. We connect this training rule to a Rao--Blackwellized objective and show that marginalizing missing entries yields a variance-weighted sensitivity penalty, discouraging over-reliance on uncertain completions. Across diverse datasets and missingness regimes, AugMask enables standard diffusion-based tabular generators to outperform specialized missing-aware baselines.
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