mlsanityData to 14 Oct 2026

Deep Knockoffs

arXiv 1811.06687 ↗First posted 16 Nov 2018stat.ME math.ST stat.APcited by 41

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Abstract (arXiv)

This paper introduces a machine for sampling approximate model-X knockoffs for arbitrary and unspecified data distributions using deep generative models. The main idea is to iteratively refine a knockoff sampling mechanism until a criterion measuring the validity of the produced knockoffs is optimized; this criterion is inspired by the popular maximum mean discrepancy in machine learning and can be thought of as measuring the distance to pairwise exchangeability between original and knockoff features. By building upon the existing model-X framework, we thus obtain a flexible and model-free statistical tool to perform controlled variable selection. Extensive numerical experiments and quantitative tests confirm the generality, effectiveness, and power of our deep knockoff machines. Finally, we apply this new method to a real study of mutations linked to changes in drug resistance in the human immunodeficiency virus.

Key facts. Cited by 41 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Nov 2018.

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Citing papers per year: 2021: 6; 2022: 14; 2023: 6; 2024: 3; 2025: 7; 2026: 5.

41 papers.

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  1. 2022Training Uncertainty-Aware Classifiers with Conformalized Deep Learningcited by 64
  2. 2022The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspectivecited by 24
  3. 2022Model-X Sequential Testing for Conditional Independence via Testing by Bettingcited by 14
  4. 2022Composite Feature Selection using Deep Ensemblescited by 11
  5. 2022Conditional Feature Importance for Mixed Datacited by 7
  6. 2023Sequential Knockoffs for Variable Selection in Reinforcement Learningcited by 7
  7. 2021Normalizing Flows for Knockoff-free Controlled Feature Selectioncited by 5
  8. 2021Kernel Two-Sample Tests in High Dimension: Interplay Between Moment Discrepancy and Dimension-and-Sample Orderscited by 5
  9. 2022Time Series Causal Link Estimation under Hidden Confounding using Knockoff Interventionscited by 5
  10. 2021Counterfactual Generation with Knockoffscited by 4
  11. 2021Multivariate rank via entropic optimal transport: sample efficiency and generative modelingcited by 4
  12. 2022Causal Discovery using Model Invariance through Knockoff Interventionscited by 4
29 more
  1. 2023Statistically Significant Concept-based Explanation of Image Classifiers via Model Knockoffscited by 4
  2. 2021Causal Inference in Non-linear Time-series using Deep Networks and Knockoff Counterfactualscited by 3
  3. 2022Kernel Biclustering algorithm in Hilbert Spacescited by 3
  4. 2022DIET: Conditional independence testing with marginal dependence measures of residual informationcited by 3
  5. 2023Efficient Multimodal Sampling via Tempered Distribution Flowcited by 3
  6. 2024High-Dimensional False Discovery Rate Control for Dependent Variablescited by 3
  7. 2022Learning to Increase the Power of Conditional Randomization Testscited by 2
  8. 2021Differentially Private Variable Selection via the Knockoff Filtercited by 1
  9. 2022Error-based Knockoffs Inference for Controlled Feature Selectioncited by 1
  10. 2023DeepROCK: Error-controlled interaction detection in deep neural networkscited by 1
  11. 2023DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging Datacited by 1
  12. 2024DeepDRK: Deep Dependency Regularized Knockoff for Feature Selectioncited by 1
  13. 2025Knoop: Practical Enhancement of Knockoff with Over-Parameterization for Variable Selectioncited by 1
  14. 2025Provable FDR Control for Deep Feature Selection: Deep MLPs and Beyondcited by 1
  15. 2022High-dimensional Inference and FDR Control for Simulated Markov Random Fieldscited by 0
  16. 2022Variable Selection with the Knockoffs: Composite Null Hypothesescited by 0
  17. 2022Error Controlled Feature Selection for Ultrahigh Dimensional and Highly Correlated Feature Space Using Deep Learningcited by 0
  18. 2023The Conditional Prediction Function: A Novel Technique to Control False Discovery Rate for Complex Modelscited by 0
  19. 2024Error-controlled non-additive interaction discovery in machine learning modelscited by 0
  20. 2025AutoNFS: Automatic Neural Feature Selectioncited by 0
  21. 2025Knockoff-Guided Compressive Sensing: A Statistical Machine Learning Framework for Support-Assured Signal Recoverycited by 0
  22. 2025AICO: Feature Significance Tests for Supervised Learningcited by 0
  23. 2025Where to Intervene: Action Selection in Deep Reinforcement Learningcited by 0
  24. 2025Sparse minimum Redundancy Maximum Relevance for feature selectioncited by 0
  25. 2026Beyond False Discovery Rate: A Stepdown Group SLOPE Approach for Grouped Variable Selectioncited by 0
  26. 2026Virtual Dummies: Enabling Scalable FDR-Controlled Variable Selection via Sequential Sampling of Null Featurescited by 0
  27. 2026Knockoffs-based False Discovery Rate Control and Simplification for Deep Neural Networkscited by 0
  28. 2026Hide&Seek: Learning to Explain in an End-to-End Differentiable Networkcited by 0
  29. 2026Testing Conditional Distribution Equality Using Generative Modelscited by 0