区分有意义缺失与可补全缺失，提升数据补全与下游任务效果
Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness
- 基于扩散模型联合建模数据与缺失掩码，迭代优化补全与缺失标签
- 在合成与真实数据上准确识别有意义缺失项，下游任务性能更优
- 适合处理含语义缺失的表格数据，如医疗、金融场景
缺失值填补是机器学习中的基础任务，现有方法通常假设所有缺失条目均为未观测到的常规值。然而，在许多现实数据集中，缺失可能源于两类不同原因：部分缺失是语义上有效的‘有意义缺失’（固有缺失），另一些则是观测过程导致的可填补缺失。本文将此区别形式化为选择性填补问题，目标是同时判断哪些缺失应保留、哪些应恢复。为此提出Diff-Joint框架，基于扩散模型联合建模表格数据与潜在缺失掩码，通过条件采样与不确定性感知聚合交替迭代，逐步优化补全值与缺失标签。在合成与真实数据集上的实验证明，Diff-Joint能有效识别有意义缺失，实现竞争性填补精度，并显著提升下游任务性能。
原文摘要 · Abstract (English)
Missing value imputation is a fundamental task in machine learning, with most existing methods assuming that all missing entries correspond to unobserved regular values. In many real-world datasets, however, missingness may arise from two distinct sources: some entries are meaningfully missing (intrinsically absent and semantically valid), while others are missing due to the observation process and should be imputed. We formalize this distinction as a selective imputation problem, where the goal is to jointly infer which missing entries should be preserved and which should be recovered. To address this challenge, we propose Diff-Joint, a diffusion-based framework that jointly models tabular data together with a latent missingness mask. The method alternates between conditional sampling and uncertainty-aware aggregation to iteratively refine both imputed values and missingness labels. Empirical results on synthetic and real-world datasets demonstrate that Diff-Joint effectively identifies meaningfully missing entries while achieving competitive imputation accuracy and improved downstream task performance.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。