mlsanityData to 14 Oct 2026

The Variational Fair Autoencoder

arXiv 1511.00830 ↗First posted 3 Nov 2015stat.ML cs.LGcited by 223

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Abstract (arXiv)

We investigate the problem of learning representations that are invariant to certain nuisance or sensitive factors of variation in the data while retaining as much of the remaining information as possible. Our model is based on a variational autoencoding architecture with priors that encourage independence between sensitive and latent factors of variation. Any subsequent processing, such as classification, can then be performed on this purged latent representation. To remove any remaining dependencies we incorporate an additional penalty term based on the "Maximum Mean Discrepancy" (MMD) measure. We discuss how these architectures can be efficiently trained on data and show in experiments that this method is more effective than previous work in removing unwanted sources of variation while maintaining informative latent representations.

Key facts. Cited by 223 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 Nov 2015.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 54; 2022: 56; 2023: 42; 2024: 29; 2025: 27; 2026: 15.

223 papers.

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  1. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  2. 2021Fair Mixup: Fairness via Interpolationcited by 109
  3. 2021A Clarification of the Nuances in the Fairness Metrics Landscapecited by 98
  4. 2021EDITS: Modeling and Mitigating Data Bias for Graph Neural Networkscited by 92
  5. 2023Fast Model Debias with Machine Unlearningcited by 72
  6. 2021Fairness in Credit Scoring: Assessment, Implementation and Profit Implicationscited by 65
  7. 2021Fair Feature Distillation for Visual Recognitioncited by 59
  8. 2022Improving the Fairness of Chest X-ray Classifierscited by 52
  9. 2021Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learningcited by 50
  10. 2021Controllable Guarantees for Fair Outcomes via Contrastive Information Estimationcited by 45
  11. 2021Towards Fair Classifiers Without Sensitive Attributes: Exploring Biases in Related Featurescited by 45
  12. 2021Fair Normalizing Flowscited by 38
38 more
  1. 2022FMP: Toward Fair Graph Message Passing against Topology Biascited by 35
  2. 2021Selective Regression Under Fairness Criteriacited by 34
  3. 2022Fairness Reprogrammingcited by 33
  4. 2023FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methodscited by 33
  5. 2022Learning Fair Representation via Distributional Contrastive Disentanglementcited by 31
  6. 2021A Bit More Bayesian: Domain-Invariant Learning with Uncertaintycited by 27
  7. 2022Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refittingcited by 26
  8. 2023Privacy and Fairness in Federated Learning: on the Perspective of Trade-offcited by 26
  9. 2021An Information-theoretic Approach to Distribution Shiftscited by 25
  10. 2024Training Unbiased Diffusion Models From Biased Datasetcited by 25
  11. 2022Minimax Demographic Group Fairness in Federated Learningcited by 24
  12. 2022Bayes-Optimal Classifiers under Group Fairnesscited by 24
  13. 2023Efficient fair PCA for fair representation learningcited by 23
  14. 2024Graph Condensation: A Surveycited by 23
  15. 2023Adversarial Bayesian Augmentation for Single-Source Domain Generalizationcited by 22
  16. 2023From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modelingcited by 22
  17. 2021Algorithm Fairness in AI for Medicine and Healthcarecited by 21
  18. 2022Transferring Fairness under Distribution Shifts via Fair Consistency Regularizationcited by 21
  19. 2022FARE: Provably Fair Representation Learning with Practical Certificatescited by 21
  20. 2022FairStyle: Debiasing StyleGAN2 with Style Channel Manipulationscited by 20
  21. 2022A Survey on Fairness for Machine Learning on Graphscited by 20
  22. 2021Fairness-aware Class Imbalanced Learningcited by 19
  23. 2021Latent Space Smoothing for Individually Fair Representationscited by 18
  24. 2021Removing biased data to improve fairness and accuracycited by 17
  25. 2021Understanding and Mitigating Accuracy Disparity in Regressioncited by 17
  26. 2021Fair Representation: Guaranteeing Approximate Multiple Group Fairness for Unknown Taskscited by 16
  27. 2022Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcarecited by 16
  28. 2022Selection, Ignorability and Challenges With Causal Fairnesscited by 16
  29. 2022Fair Bayes-Optimal Classifiers Under Predictive Paritycited by 16
  30. 2021Generative Adversarial Networks: A Survey Towards Private and Secure Applicationscited by 15
  31. 2021Metadata Normalizationcited by 14
  32. 2021Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classificationcited by 13
  33. 2021Algorithmic Bias and Data Bias: Understanding the Relation between Distributionally Robust Optimization and Data Curationcited by 13
  34. 2022Moment Matching Deep Contrastive Latent Variable Modelscited by 13
  35. 2022Don't Throw it Away! The Utility of Unlabeled Data in Fair Decision Makingcited by 13
  36. 2022MMD-B-Fair: Learning Fair Representations with Statistical Testingcited by 13
  37. 2021Learning Domain Invariant Representations in Goal-conditioned Block MDPscited by 12
  38. 2022Bottlenecks CLUB: Unifying Information-Theoretic Trade-offs Among Complexity, Leakage, and Utilitycited by 12

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