Data to 7 Oct 2026

not-MIWAE: Deep Generative Modelling with Missing not at Random Data

arXiv 2006.12871 ↗First posted 23 Jun 2020stat.ML cs.LG stat.MEcited by 61

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Abstract (arXiv)

When a missing process depends on the missing values themselves, it needs to be explicitly modelled and taken into account while doing likelihood-based inference. We present an approach for building and fitting deep latent variable models (DLVMs) in cases where the missing process is dependent on the missing data. Specifically, a deep neural network enables us to flexibly model the conditional distribution of the missingness pattern given the data. This allows for incorporating prior information about the type of missingness (e.g. self-censoring) into the model. Our inference technique, based on importance-weighted variational inference, involves maximising a lower bound of the joint likelihood. Stochastic gradients of the bound are obtained by using the reparameterisation trick both in latent space and data space. We show on various kinds of data sets and missingness patterns that explicitly modelling the missing process can be invaluable.

Key facts. Cited by 61 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jun 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 13; 2023: 10; 2024: 7; 2025: 12; 2026: 13.

61 papers.

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2023Ambient Diffusion: Learning Clean Distributions from Corrupted Datacited by 108
2022HyperImpute: Generalized Iterative Imputation with Automatic Model Selectioncited by 81
2023MissDiff: Training Diffusion Models on Tabular Data with Missing Valuescited by 38
2021Identifiable Generative Models for Missing Not at Random Data Imputationcited by 30
2023Probabilistic Imputation for Time-series Classification with Missing Datacited by 21
2022Model-agnostic out-of-distribution detection using combined statistical testscited by 14
2022Learning Conditional Variational Autoencoders with Missing Covariatescited by 10
2021Spatiotemporal Tensor Completion for Improved Urban Traffic Imputationcited by 9
2022A Brief Overview of Unsupervised Neural Speech Representation Learningcited by 9
2024Review for Handling Missing Data with special missing mechanismcited by 7
2021EMFlow: Data Imputation in Latent Space via EM and Deep Flow Modelscited by 6
2022Differentiable and Scalable Generative Adversarial Models for Data Imputationcited by 5

38 more

2025Robust Simulation-Based Inference under Missing Data via Neural Processescited by 5
2021Variational Gibbs Inference for Statistical Model Estimation from Incomplete Datacited by 4
2022Posterior Matching for Arbitrary Conditioningcited by 4
2022FragmGAN: Generative Adversarial Nets for Fragmentary Data Imputation and Predictioncited by 4
2023Learning multi-modal generative models with permutation-invariant encoders and tighter variational objectivescited by 4
2025Tractable Representation Learning with Probabilistic Circuitscited by 4
2021Deep Generative Pattern-Set Mixture Models for Nonignorable Missingnesscited by 3
2022Uphill Roads to Variational Tightness: Monotonicity and Monte Carlo Objectivescited by 3
2022Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous Datasetscited by 3
2022Leveraging variational autoencoders for multiple data imputationcited by 3
2022Multiple Imputation with Neural Network Gaussian Process for High-dimensional Incomplete Datacited by 3
2025CACTI: Leveraging Copy Masking and Contextual Information to Improve Tabular Data Imputationcited by 3
2022Reconstruction of Incomplete Wildfire Data using Deep Generative Modelscited by 2
2023Fed-MIWAE: Federated Imputation of Incomplete Data via Deep Generative Modelscited by 2
2024Improving Variational Autoencoder Estimation from Incomplete Data with Mixture Variational Familiescited by 2
2025HI-PMK: A Data-Dependent Kernel for Incomplete Heterogeneous Data Representationcited by 2
2025Missing Data in Signal Processing and Machine Learning: Models, Methods and Modern Approachescited by 2
2025Incomplete Data, Complete Dynamics: A Diffusion Approachcited by 2
2025One Pipeline, Many Transformers: Pattern-Specific Imputation Specialists for Tabular Missing Datacited by 2
2026Missingness-aware Data Imputation via AI-powered Bayesian Generative Modelingcited by 2
2022PROMISSING: Pruning Missing Values in Neural Networkscited by 1
2023Generative Adversarial Classification Network with Application to Network Traffic Classificationcited by 1
2023No Imputation Needed: A Switch Approach to Irregularly Sampled Time Seriescited by 1
2023Posterior Consistency for Missing Data in Variational Autoencoderscited by 1
2023Estimating treatment effects from single-arm trials via latent-variable modelingcited by 1
2024Latent variable model for high-dimensional point process with structured missingnesscited by 1
2024Masked Language Modeling Becomes Conditional Density Estimation for Tabular Data Synthesiscited by 1
2024Exploiting Missing Data Remediation Strategies using Adversarial Missingness Attackscited by 1
2024Still More Shades of Null: An Evaluation Suite for Responsible Missing Value Imputationcited by 1
2025Developing robust methods to handle missing data in real-world applications effectivelycited by 1
2025Impute-MACFM: Imputation based on Mask-Aware Flow Matchingcited by 1
2025IVGAE: Handling Incomplete Heterogeneous Data with a Variational Graph Autoencodercited by 1
2026AugMask: Training Diffusion Models on Incomplete Tabular Data via Stochastic Augmentation and Maskingcited by 1
2021Deep Learning-Based Estimation and Goodness-of-Fit for Large-Scale Confirmatory Item Factor Analysiscited by 0
2023Deep Generative Imputation Model for Missing Not At Random Datacited by 0
2024Learning from Complementary Featurescited by 0
2025Domain Adaptation Under MNAR Missingnesscited by 0
2025Deep Latent Variable Model based Vertical Federated Learning with Flexible Alignment and Labeling Scenarioscited by 0

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