Hugging Face Trending Papers June 3, 2026

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

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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.

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diffusion

arXiv Machine Learning

Jun 4

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arXiv Machine Learning

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rag diffusion multimodal benchmarks

Related stories

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

AugMask: Training Diffusion Models on Incomplete Tabular Data via Stochastic Augmentation and Masking

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