Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness

ArXi:2606.05073v1 Announce Type: new 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.