mlsanityData to 14 Oct 2026

CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inference

arXiv 2310.14608 ↗First posted 23 Oct 2023stat.ML cs.LGcited by 10

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Abstract (arXiv)

We propose a novel statistical method for testing the results of anomaly detection (AD) under domain adaptation (DA), which we call CAD-DA -- controllable AD under DA. The distinct advantage of the CAD-DA lies in its ability to control the probability of misidentifying anomalies under a pre-specified level α (e.g., 0.05). The challenge within this DA setting is the necessity to account for the influence of DA to ensure the validity of the inference results. Our solution to this challenge leverages the concept of conditional Selective Inference to handle the impact of DA. To our knowledge, this is the first work capable of conducting a valid statistical inference within the context of DA. We evaluate the performance of the CAD-DA method on both synthetic and real-world datasets.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 26 of its 61 references that are in the map. First posted on arXiv 23 Oct 2023.

Builds on

26 of 61 references.

References · 26

  1. 2015Optimal Transport for Domain Adaptationcited by 463
  2. 2013Exact post-selection inference, with application to the lassocited by 91
  3. 2014Optimal Inference After Model Selectioncited by 67
  4. 2014Exact Post-Selection Inference for Sequential Regression Procedurescited by 48
  5. 2020Selective Inference for Hierarchical Clusteringcited by 39
  6. 2020A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applicationscited by 33
  7. 2017Valid Inference Corrected for Outlier Removalcited by 26
  8. 2019Testing for a Change in Mean After Changepoint Detectioncited by 26
  9. 2020Computing Valid p-value for Optimal Changepoint by Selective Inference using Dynamic Programmingcited by 25
  10. 2021More Powerful Conditional Selective Inference for Generalized Lasso by Parametric Programmingcited by 25
  11. 2019Computing Valid p-values for Image Segmentation by Selective Inferencecited by 24
  12. 2018More powerful post-selection inference, with application to the Lassocited by 23
14 more
  1. 2022Selective inference for k-means clusteringcited by 22
  2. 2016Selective Inference for Group-Sparse Linear Modelscited by 21
  3. 2020Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective Inferencecited by 21
  4. 2018Post-Selection Inference for Changepoint Detection Algorithms with Application to Copy Number Variation Datacited by 19
  5. 2014A significance test for forward stepwise model selectioncited by 17
  6. 2015Selective Sequential Model Selectioncited by 17
  7. 2023Valid P-Value for Deep Learning-Driven Salient Regioncited by 17
  8. 2021Conditional Selective Inference for Robust Regression and Outlier Detection using Piecewise-Linear Homotopy Continuationcited by 16
  9. 2021Fast and More Powerful Selective Inference for Sparse High-order Interaction Modelcited by 15
  10. 2021Tree-Values: selective inference for regression treescited by 15
  11. 2016Post Selection Inference with Kernelscited by 13
  12. 2017Selective Inference for Change Point Detection in Multi-dimensional Sequencescited by 11
  13. 2021Valid and Exact Statistical Inference for Multi-dimensional Multiple Change-Points by Selective Inferencecited by 10
  14. 2021Exact Statistical Inference for the Wasserstein Distance by Selective Inferencecited by 7

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Cited by year

Citing papers per year: 2024: 6; 2025: 4.

10 papers.

  1. 2024Statistical Test for Anomaly Detections by Variational Auto-Encoderscited by 7
  2. 2024Statistical Test for Feature Selection Pipelines by Selective Inferencecited by 3
  3. 2024Statistical Test for Auto Feature Engineering by Selective Inferencecited by 3
  4. 2025Quantifying Statistical Significance of Deep Nearest Neighbor Anomaly Detection via Selective Inferencecited by 3
  5. 2024Statistical Inference for Feature Selection after Optimal Transport-based Domain Adaptationcited by 2
  6. 2024Controllable RANSAC-based Anomaly Detection via Hypothesis Testingcited by 2
  7. 2025Change Point Detection in the Frequency Domain with Statistical Reliabilitycited by 2
  8. 2025Statistical Inference for Autoencoder-based Anomaly Detection after Representation Learning-based Domain Adaptationcited by 2
  9. 2024Statistical Test for Diffusion-Based Anomaly Localization via Selective Inferencecited by 1
  10. 2025Post-Transfer Learning Statistical Inference in High-Dimensional Regressioncited by 0