mlsanityData to 14 Oct 2026

Transformed Distribution Matching for Missing Value Imputation

arXiv 2302.10363 ↗First posted 20 Feb 2023cs.LGcited by 31

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Abstract (arXiv)

We study the problem of imputing missing values in a dataset, which has important applications in many domains. The key to missing value imputation is to capture the data distribution with incomplete samples and impute the missing values accordingly. In this paper, by leveraging the fact that any two batches of data with missing values come from the same data distribution, we propose to impute the missing values of two batches of samples by transforming them into a latent space through deep invertible functions and matching them distributionally. To learn the transformations and impute the missing values simultaneously, a simple and well-motivated algorithm is proposed. Our algorithm has fewer hyperparameters to fine-tune and generates high-quality imputations regardless of how missing values are generated. Extensive experiments over a large number of datasets and competing benchmark algorithms show that our method achieves state-of-the-art performance.

Key facts. Cited by 31 papers in the map (data through 14 Oct 2026). Builds on 54 of its 91 references that are in the map. First posted on arXiv 20 Feb 2023.

Builds on

54 of 91 references.

References · 54

  1. 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
  2. 2022Generative Adversarial Networkscited by 5,627
  3. 2013Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distancescited by 2,316
  4. 2016Density estimation using Real NVPcited by 2,042
  5. 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,545
  6. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,323
  7. 2018Learning deep representations by mutual information estimation and maximizationcited by 1,273
  8. 2014NICE: Non-linear Independent Components Estimationcited by 1,058
  9. 2019Learning Representations by Maximizing Mutual Information Across Viewscited by 734
  10. 2017Self-Normalizing Neural Networkscited by 598
  11. 2019On Variational Bounds of Mutual Informationcited by 559
  12. 2015Importance Weighted Autoencoderscited by 456
38 more
  1. 2018GAIN: Missing Data Imputation using Generative Adversarial Netscited by 352
  2. 2019On Mutual Information Maximization for Representation Learningcited by 316
  3. 2017Learning Generative Models with Sinkhorn Divergencescited by 314
  4. 2018Analyzing Inverse Problems with Invertible Neural Networkscited by 242
  5. 2017Near-linear time approximation algorithms for optimal transport via Sinkhorn iterationcited by 223
  6. 2017The Reversible Residual Network: Backpropagation Without Storing Activationscited by 217
  7. 2013Fast Computation of Wasserstein Barycenterscited by 216
  8. 2021OTA: Optimal Transport Assignment for Object Detectioncited by 178
  9. 2018Handling Incomplete Heterogeneous Data using VAEscited by 149
  10. 2020Handling Missing Data with Graph Representation Learningcited by 133
  11. 2018Computational Optimal Transport: Complexity by Accelerated Gradient Descent Is Better Than by Sinkhorn's Algorithmcited by 112
  12. 2014Matrix Completion and Low-Rank SVD via Fast Alternating Least Squarescited by 104
  13. 2020Missing Data Imputation using Optimal Transportcited by 100
  14. 2022HyperImpute: Generalized Iterative Imputation with Automatic Model Selectioncited by 83
  15. 2019MisGAN: Learning from Incomplete Data with Generative Adversarial Networkscited by 75
  16. 2018Variational Autoencoder with Arbitrary Conditioningcited by 68
  17. 2018Excessive Invariance Causes Adversarial Vulnerabilitycited by 66
  18. 2021MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanismscited by 51
  19. 2020Neural Topic Model via Optimal Transportcited by 50
  20. 2020DeepEMD: Differentiable Earth Mover's Distance for Few-Shot Learningcited by 49
  21. 2020MCFlow: Monte Carlo Flow Models for Data Imputationcited by 37
  22. 2022A Unified Wasserstein Distributional Robustness Framework for Adversarial Trainingcited by 36
  23. 2022Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddingscited by 36
  24. 2022Learning to Re-weight Examples with Optimal Transport for Imbalanced Classificationcited by 23
  25. 2023Vector Quantized Wasserstein Auto-Encodercited by 22
  26. 2017MIDA: Multiple Imputation using Denoising Autoencoderscited by 20
  27. 2021Learning Prototype-oriented Set Representations for Meta-Learningcited by 19
  28. 2022Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlocited by 19
  29. 2013What Is Meant by "Missing at Random"?cited by 16
  30. 2021Simultaneous Missing Value Imputation and Structure Learning with Groupscited by 16
  31. 2021Multiple Imputation via Generative Adversarial Network for High-dimensional Blockwise Missing Value Problemscited by 15
  32. 2022Adaptive Distribution Calibration for Few-Shot Learning with Hierarchical Optimal Transportcited by 14
  33. 2022Handling Missing Data via Max-Entropy Regularized Graph Autoencodercited by 12
  34. 2021Variational Selective Autoencoder: Learning from Partially-Observed Heterogeneous Datacited by 10
  35. 2019R-miss-tastic: a unified platform for missing values methods and workflowscited by 8
  36. 2021EMFlow: Data Imputation in Latent Space via EM and Deep Flow Modelscited by 6
  37. 2022GEDI: A Graph-based End-to-end Data Imputation Frameworkcited by 5
  38. 2022FragmGAN: Generative Adversarial Nets for Fragmentary Data Imputation and Predictioncited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 14; 2025: 8; 2026: 9.

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  1. 2024LLM-Forest: Ensemble Learning of LLMs with Graph-Augmented Prompts for Data Imputationcited by 25
  2. 2024DiffPuter: Empowering Diffusion Models for Missing Data Imputationcited by 19
  3. 2024LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language Modelscited by 11
  4. 2025On LLM-Enhanced Mixed-Type Data Imputation with High-Order Message Passingcited by 11
  5. 2024Neural Topic Modeling with Large Language Models in the Loopcited by 10
  6. 2024Higher-order Spatio-temporal Physics-incorporated Graph Neural Network for Multivariate Time Series Imputationcited by 8
  7. 2024Optimal Transport for Structure Learning Under Missing Datacited by 6
  8. 2025Missing Data Imputation by Reducing Mutual Information with Rectified Flowscited by 6
  9. 2024A Context-Aware Approach for Enhancing Data Imputation with Pre-trained Language Modelscited by 5
  10. 2025Robust Simulation-Based Inference under Missing Data via Neural Processescited by 5
  11. 2026Cascaded Flow Matching for Heterogeneous Tabular Data with Mixed-Type Featurescited by 4
  12. 2024Rethinking the Diffusion Models for Numerical Tabular Data Imputation from the Perspective of Wasserstein Gradient Flowcited by 3
19 more
  1. 2025Impute-MACFM: Imputation based on Mask-Aware Flow Matchingcited by 2
  2. 2024Scalable Numerical Embeddings for Multivariate Time Series: Enhancing Healthcare Data Representation Learningcited by 1
  3. 2024Masked Language Modeling Becomes Conditional Density Estimation for Tabular Data Synthesiscited by 1
  4. 2024Still More Shades of Null: An Evaluation Suite for Responsible Missing Value Imputationcited by 1
  5. 2024mDAE : modified Denoising AutoEncoder for missing data imputationcited by 1
  6. 2025OASIS: Harnessing Diffusion Adversarial Network for Ocean Salinity Imputation using Sparse Drifter Trajectoriescited by 1
  7. 2024A parameter-free clustering algorithm for missing datasetscited by 0
  8. 2024A Survey and Benchmarking of Spatial-Temporal Traffic Data Imputation Modelscited by 0
  9. 2025IMAGIC-500: IMputation benchmark on A Generative Imaginary Country (500k samples)cited by 0
  10. 2025Leveraging AI Graders for Missing Score Imputation to Achieve Accurate Ability Estimation in Constructed-Response Testscited by 0
  11. 2025An Interdisciplinary and Cross-Task Review on Missing Data Imputationcited by 0
  12. 2026Nearly Optimal Bayesian Inference for Structural Missingnesscited by 0
  13. 2026Analyzing and Improving Diffusion Models for Time-Series Data Imputation: A Proximal Recursion Perspectivecited by 0
  14. 2026Theoretical Analysis of Measure Consistency Regularization for Partially Observed Datacited by 0
  15. 2026Harpoon: Generalised Manifold Guidance for Conditional Tabular Diffusioncited by 0
  16. 2026Neural Optimal Transport in Hilbert Spaces: Characterizing Spurious Solutions and Gaussian Smoothingcited by 0
  17. 2026Missing Pattern Recognized Diffusion Imputation Model for Missing Not At Randomcited by 0
  18. 2026SafeImpute: Reliable Clinical Data Imputation via Conformal Selectioncited by 0
  19. 2026Imputation Meets Clustering: Exploiting Latent Subgroup Structure for Missing Data Recoverycited by 0