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missing-data-imputation

topic1 events
papersTODAY 04:00 UTC

Diffusion Model Imputes Missing Values in Mixed Numerical and Categorical Data

A new arXiv paper introduces Impute-EM, a diffusion-based approach for filling in missing values in datasets that mix numerical, categorical, and binary variables. Most existing diffusion imputation methods convert discrete variables into continuous stand-ins, which the authors argue is a limitation the native mixed-state design avoids. The work targets heterogeneous data mining settings where such mixed variable types commonly appear together.

diffusion-models/data-imputation/missing-data/research