2025/06/10 by Vaidotas Šimkus, Michael U. Gutmann, Simkus, Vaidotas +1 · 2 citations
Computer Science · Mathematics · Medicine · #62D10 #Advanced Neuroimaging Techniques and Applications #FOS: Computer and information sciences #I.5.1 #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Healthcare #Statistical Methods and Bayesian Inference
paper · pdf · doi:10.48550/arxiv.2506.09258
openalex publication_date 2025/06/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.