mlsanityData to 14 Oct 2026

MIDA: Multiple Imputation using Denoising Autoencoders

arXiv 1705.02737 ↗First posted 8 May 2017cs.LG stat.MLcited by 20

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Abstract (arXiv)

Missing data is a significant problem impacting all domains. State-of-the-art framework for minimizing missing data bias is multiple imputation, for which the choice of an imputation model remains nontrivial. We propose a multiple imputation model based on overcomplete deep denoising autoencoders. Our proposed model is capable of handling different data types, missingness patterns, missingness proportions and distributions. Evaluation on several real life datasets show our proposed model significantly outperforms current state-of-the-art methods under varying conditions while simultaneously improving end of the line analytics.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 May 2017.

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Citing papers per year: 2021: 4; 2022: 7; 2023: 6; 2024: 3.

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  1. 2022HyperImpute: Generalized Iterative Imputation with Automatic Model Selectioncited by 83
  2. 2021MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanismscited by 51
  3. 2023Transformed Distribution Matching for Missing Value Imputationcited by 31
  4. 2022Data Imputation with Iterative Graph Reconstructioncited by 15
  5. 2024Automated data processing and feature engineering for deep learning and big data applications: a surveycited by 14
  6. 2021EMFlow: Data Imputation in Latent Space via EM and Deep Flow Modelscited by 6
  7. 2023TS-Diffusion: Generating Highly Complex Time Series with Diffusion Modelscited by 6
  8. 2022Differentiable and Scalable Generative Adversarial Models for Data Imputationcited by 5
  9. 2024Explainability of Machine Learning Models under Missing Datacited by 5
  10. 2021MisConv: Convolutional Neural Networks for Missing Datacited by 4
  11. 2022FragmGAN: Generative Adversarial Nets for Fragmentary Data Imputation and Predictioncited by 4
  12. 2022Deep Learning in Single-Cell Analysiscited by 4
8 more
  1. 2022STING: Self-attention based Time-series Imputation Networks using GANcited by 3
  2. 2022Principal Component Analysis based frameworks for efficient missing data imputation algorithmscited by 2
  3. 2023Conditional expectation with regularization for missing data imputationcited by 2
  4. 2023Blockwise Principal Component Analysis for monotone missing data imputation and dimensionality reductioncited by 2
  5. 2023Towards Better Modeling with Missing Data: A Contrastive Learning-based Visual Analytics Perspectivecited by 1
  6. 2021DPER: Efficient Parameter Estimation for Randomly Missing Datacited by 0
  7. 2023Imputation using training labels and classification via label imputationcited by 0
  8. 2024Enhancing Missing Data Imputation through Combined Bipartite Graph and Complete Directed Graphcited by 0