mlsanityData to 14 Oct 2026

Multiple Imputation via Generative Adversarial Network for High-dimensional Blockwise Missing Value Problems

arXiv 2112.11507 ↗First posted 21 Dec 2021cs.LG stat.APcited by 15

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Abstract (arXiv)

Missing data are present in most real world problems and need careful handling to preserve the prediction accuracy and statistical consistency in the downstream analysis. As the gold standard of handling missing data, multiple imputation (MI) methods are proposed to account for the imputation uncertainty and provide proper statistical inference. In this work, we propose Multiple Imputation via Generative Adversarial Network (MI-GAN), a deep learning-based (in specific, a GAN-based) multiple imputation method, that can work under missing at random (MAR) mechanism with theoretical support. MI-GAN leverages recent progress in conditional generative adversarial neural works and shows strong performance matching existing state-of-the-art imputation methods on high-dimensional datasets, in terms of imputation error. In particular, MI-GAN significantly outperforms other imputation methods in the sense of statistical inference and computational speed.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 8 of its 28 references that are in the map. First posted on arXiv 21 Dec 2021.

Builds on

8 of 28 references.

References · 8

  1. 2017Improved Training of Wasserstein GANscited by 2,235
  2. 2018GAIN: Missing Data Imputation using Generative Adversarial Netscited by 352
  3. 2014Matrix Completion and Low-Rank SVD via Fast Alternating Least Squarescited by 104
  4. 2020Missing Data Imputation using Optimal Transportcited by 100
  5. 2019MisGAN: Learning from Incomplete Data with Generative Adversarial Networkscited by 75
  6. 2018Variational Autoencoder with Arbitrary Conditioningcited by 68
  7. 2019Inference and Uncertainty Quantification for Noisy Matrix Completioncited by 39
  8. 2019CollaGAN : Collaborative GAN for Missing Image Data Imputationcited by 33

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Cited by year

Citing papers per year: 2022: 5; 2023: 4; 2024: 5; 2025: 1.

15 papers.

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  1. 2022HyperImpute: Generalized Iterative Imputation with Automatic Model Selectioncited by 83
  2. 2023Transformed Distribution Matching for Missing Value Imputationcited by 31
  3. 2022Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniquescited by 25
  4. 2024Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AIcited by 15
  5. 2024Automated data processing and feature engineering for deep learning and big data applications: a surveycited by 14
  6. 2022A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sourcescited by 13
  7. 2024Optimal Transport for Structure Learning Under Missing Datacited by 6
  8. 2024A Context-Aware Approach for Enhancing Data Imputation with Pre-trained Language Modelscited by 5
  9. 2025CACTI: Leveraging Copy Masking and Contextual Information to Improve Tabular Data Imputationcited by 4
  10. 2022Multiple Imputation with Neural Network Gaussian Process for High-dimensional Incomplete Datacited by 3
  11. 2023Prediction approaches for partly missing multi-omics covariate data: A literature review and an empirical comparison studycited by 2
  12. 2023Adversarial Learning for Feature Shift Detection and Correctioncited by 2
3 more
  1. 2022Imputation of missing values in multi-view datacited by 1
  2. 2023MISNN: Multiple Imputation via Semi-parametric Neural Networkscited by 0
  3. 2024A Survey and Benchmarking of Spatial-Temporal Traffic Data Imputation Modelscited by 0