mlsanityData to 7 Oct 2026

MCFlow: Monte Carlo Flow Models for Data Imputation

arXiv 2003.12628 ↗First posted 27 Mar 2020cs.LG cs.CV stat.MLcited by 37

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Abstract (arXiv)

We consider the topic of data imputation, a foundational task in machine learning that addresses issues with missing data. To that end, we propose MCFlow, a deep framework for imputation that leverages normalizing flow generative models and Monte Carlo sampling. We address the causality dilemma that arises when training models with incomplete data by introducing an iterative learning scheme which alternately updates the density estimate and the values of the missing entries in the training data. We provide extensive empirical validation of the effectiveness of the proposed method on standard multivariate and image datasets, and benchmark its performance against state-of-the-art alternatives. We demonstrate that MCFlow is superior to competing methods in terms of the quality of the imputed data, as well as with regards to its ability to preserve the semantic structure of the data.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Mar 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 4; 2023: 6; 2024: 6; 2025: 7; 2026: 7.

37 papers.

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  1. 2022SAITS: Self-Attention-based Imputation for Time Seriescited by 167
  2. 2023Ambient Diffusion: Learning Clean Distributions from Corrupted Datacited by 108
  3. 2022HyperImpute: Generalized Iterative Imputation with Automatic Model Selectioncited by 81
  4. 2023MissDiff: Training Diffusion Models on Tabular Data with Missing Valuescited by 38
  5. 2023Transformed Distribution Matching for Missing Value Imputationcited by 31
  6. 2024DiffPuter: Empowering Diffusion Models for Missing Data Imputationcited by 17
  7. 2024MTSCI: A Conditional Diffusion Model for Multivariate Time Series Consistent Imputationcited by 13
  8. 2023Squared Neural Families: A New Class of Tractable Density Modelscited by 12
  9. 2021DAEMA: Denoising Autoencoder with Mask Attentioncited by 8
  10. 2025Score Matching With Missing Datacited by 7
  11. 2021EMFlow: Data Imputation in Latent Space via EM and Deep Flow Modelscited by 6
  12. 2025Missing Data Imputation by Reducing Mutual Information with Rectified Flowscited by 6
25 more
  1. 2024Optimal Transport for Structure Learning Under Missing Datacited by 5
  2. 2024A Context-Aware Approach for Enhancing Data Imputation with Pre-trained Language Modelscited by 5
  3. 2022FragmGAN: Generative Adversarial Nets for Fragmentary Data Imputation and Predictioncited by 4
  4. 2024FastSTI: A Fast Conditional Pseudo Numerical Diffusion Model for Spatio-temporal Traffic Data Imputationcited by 4
  5. 2024MagiNet: Mask-Aware Graph Imputation Network for Incomplete Traffic Datacited by 3
  6. 2025CACTI: Leveraging Copy Masking and Contextual Information to Improve Tabular Data Imputationcited by 3
  7. 2021Training Deep Normalizing Flow Models in Highly Incomplete Data Scenarios with Prior Regularizationcited by 1
  8. 2021Semi-supervised Learning with Missing Values Imputationcited by 1
  9. 2021Viscos Flows: Variational Schur Conditional Sampling With Normalizing Flowscited by 1
  10. 2021Missingness Augmentation: A General Approach for Improving Generative Imputation Modelscited by 1
  11. 2025Impute-MACFM: Imputation based on Mask-Aware Flow Matchingcited by 1
  12. 2026CoreFlow: Low-Rank Matrix Generative Modelscited by 1
  13. 2021Deep Distribution-preserving Incomplete Clustering with Optimal Transportcited by 0
  14. 2022Towards a methodology for addressing missingness in datasets, with an application to demographic health datasetscited by 0
  15. 2023Monotone deep Boltzmann machinescited by 0
  16. 2023FlexSSL : A Generic and Efficient Framework for Semi-Supervised Learningcited by 0
  17. 2025NeuralPrefix: A Zero-shot Sensory Data Imputation Plugincited by 0
  18. 2025Global Ease of Living Index: a machine learning framework for longitudinal analysis of major economiescited by 0
  19. 2025An Interdisciplinary and Cross-Task Review on Missing Data Imputationcited by 0
  20. 2026Nearly Optimal Bayesian Inference for Structural Missingnesscited by 0
  21. 2026Order-Agnostic Autoregressive Modelling with Missing Datacited by 0
  22. 2026Missing Pattern Recognized Diffusion Imputation Model for Missing Not At Randomcited by 0
  23. 2026Latent Diffusion for Missing Datacited by 0
  24. 2026TabSODA: Tabular Diffusion based Imputation with Skip Pattern Detection and Ordinal Awarenesscited by 0
  25. 2026Impute-EM: Native Mixed-State Diffusion Models for Heterogeneous Data Imputationcited by 0