CFMI: Flow Matching for Missing Data Imputation

• URL: http://arxiv.org/abs/2506.09258v1
• Date: Tue, 10 Jun 2025 21:40:36 GMT
• Title: CFMI: Flow Matching for Missing Data Imputation
• Authors: Vaidotas Simkus, Michael U. Gutmann,
• Abstract summary: We introduce conditional flow matching for imputation (CFMI), a new general-purpose method to impute missing data.<n>CFMI combines continuous normalising flows, flow-matching, and shared conditional modelling to deal with intractabilities of traditional multiple imputation.
• Score: 6.2279613160361995
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce conditional flow matching for imputation (CFMI), a new general-purpose method to impute missing data. The method combines continuous normalising flows, flow-matching, and shared conditional modelling to deal with intractabilities of traditional multiple imputation. Our comparison with nine classical and state-of-the-art imputation methods on 24 small to moderate-dimensional tabular data sets shows that CFMI matches or outperforms both traditional and modern techniques across a wide range of metrics. Applying the method to zero-shot imputation of time-series data, we find that it matches the accuracy of a related diffusion-based method while outperforming it in terms of computational efficiency. Overall, CFMI performs at least as well as traditional methods on lower-dimensional data while remaining scalable to high-dimensional settings, matching or exceeding the performance of other deep learning-based approaches, making it a go-to imputation method for a wide range of data types and dimensionalities.

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