mlsanityData to 7 Oct 2026

Variational Autoencoder with Arbitrary Conditioning

arXiv 1806.02382 ↗First posted 6 Jun 2018stat.ML cs.LGcited by 68

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Abstract (arXiv)

We propose a single neural probabilistic model based on variational autoencoder that can be conditioned on an arbitrary subset of observed features and then sample the remaining features in "one shot". The features may be both real-valued and categorical. Training of the model is performed by stochastic variational Bayes. The experimental evaluation on synthetic data, as well as feature imputation and image inpainting problems, shows the effectiveness of the proposed approach and diversity of the generated samples.

Key facts. Cited by 68 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jun 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 19; 2022: 11; 2023: 11; 2024: 9; 2025: 10; 2026: 8.

68 papers.

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  1. 2022GCNet: Graph Completion Network for Incomplete Multimodal Learning in Conversationcited by 86
  2. 2021Efficient Neural Causal Discovery without Acyclicity Constraintscited by 82
  3. 2022HyperImpute: Generalized Iterative Imputation with Automatic Model Selectioncited by 81
  4. 2022Learning to Induce Causal Structurecited by 68
  5. 2023Monte Carlo guided Diffusion for Bayesian linear inverse problemscited by 65
  6. 2023Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoderscited by 32
  7. 2023Transformed Distribution Matching for Missing Value Imputationcited by 31
  8. 2021Conditional Image Generation by Conditioning Variational Auto-Encoderscited by 25
  9. 2024At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligencecited by 25
  10. 2021Arbitrary Conditional Distributions with Energycited by 22
  11. 2021Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Featurescited by 19
  12. 2021Learning Multimodal VAEs through Mutual Supervisioncited by 18
38 more
  1. 2023Acquisition Conditioned Oracle for Nongreedy Active Feature Acquisitioncited by 17
  2. 2021Simultaneous Missing Value Imputation and Structure Learning with Groupscited by 16
  3. 2025A Survey on Active Feature Acquisition Strategiescited by 16
  4. 2021Multiple Imputation via Generative Adversarial Network for High-dimensional Blockwise Missing Value Problemscited by 15
  5. 2023A Comparative Study of Methods for Estimating Conditional Shapley Values and When to Use Themcited by 15
  6. 2021Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Modelscited by 14
  7. 2021Controllable and Diverse Text Generation in E-commercecited by 13
  8. 2023Versatile Neural Processes for Learning Implicit Neural Representationscited by 11
  9. 2021Variational Selective Autoencoder: Learning from Partially-Observed Heterogeneous Datacited by 10
  10. 2021Deep Image Synthesis from Intuitive User Input: A Review and Perspectivescited by 8
  11. 2024A Context-Aware Approach for Enhancing Data Imputation with Pre-trained Language Modelscited by 5
  12. 2025shapr: Explaining Machine Learning Models with Conditional Shapley Values in R and Pythoncited by 5
  13. 2021δ-CLUE: Diverse Sets of Explanations for Uncertainty Estimatescited by 4
  14. 2022Posterior Matching for Arbitrary Conditioningcited by 4
  15. 2022FragmGAN: Generative Adversarial Nets for Fragmentary Data Imputation and Predictioncited by 4
  16. 2023Predictive World Models from Real-World Partial Observationscited by 4
  17. 2023Generative Marginalization Modelscited by 4
  18. 2024Knockout: A simple way to handle missing inputscited by 4
  19. 2025Imputation-Powered Inferencecited by 4
  20. 2021Partially Observed Exchangeable Modelingcited by 3
  21. 2022Multiple Imputation with Neural Network Gaussian Process for High-dimensional Incomplete Datacited by 3
  22. 2023Towards Invertible Semantic-Preserving Embeddings of Logical Formulaecited by 3
  23. 2024stl2vec: Semantic and Interpretable Vector Representation of Temporal Logiccited by 3
  24. 2024Distribution Guided Active Feature Acquisitioncited by 3
  25. 2021A Contextual Latent Space Model: Subsequence Modulation in Melodic Sequencecited by 2
  26. 2022Gradient-based Counterfactual Explanations using Tractable Probabilistic Modelscited by 2
  27. 2024Variational Autoencoders for Efficient Simulation-Based Inferencecited by 2
  28. 2025Morphology-Aware Peptide Discovery via Masked Conditional Generative Modelingcited by 2
  29. 2025Beyond Accuracy: An Empirical Study of Uncertainty Estimation in Imputationcited by 2
  30. 2026An AI-powered Bayesian Generative Modeling Approach for Arbitrary Conditional Inferencecited by 2
  31. 2026Missingness-aware Data Imputation via AI-powered Bayesian Generative Modelingcited by 2
  32. 2021Unsupervised Imputation of Non-ignorably Missing Data Using Importance-Weighted Autoencoderscited by 1
  33. 2021Relational VAE: A Continuous Latent Variable Model for Graph Structured Datacited by 1
  34. 2021A Modulation Layer to Increase Neural Network Robustness Against Data Quality Issuescited by 1
  35. 2022Electra: Conditional Generative Model based Predicate-Aware Query Approximationcited by 1
  36. 2022Interpolation of Missing Swaption Volatility Data using Gibbs Sampling on Variational Autoencoderscited by 1
  37. 2022Explainability as statistical inferencecited by 1
  38. 2022Probabilistic Shape Completion by Estimating Canonical Factors with Hierarchical VAEcited by 1

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