mlsanityData to 7 Oct 2026

Meta-Learning for Unsupervised Outlier Detection with Optimal Transport

arXiv 2211.00372 ↗First posted 1 Nov 2022cs.LG cs.AIcited by 5

Not on the map. Why?

Abstract (arXiv)

Automated machine learning has been widely researched and adopted in the field of supervised classification and regression, but progress in unsupervised settings has been limited. We propose a novel approach to automate outlier detection based on meta-learning from previous datasets with outliers. Our premise is that the selection of the optimal outlier detection technique depends on the inherent properties of the data distribution. We leverage optimal transport in particular, to find the dataset with the most similar underlying distribution, and then apply the outlier detection techniques that proved to work best for that data distribution. We evaluate the robustness of our approach and find that it outperforms the state of the art methods in unsupervised outlier detection. This approach can also be easily generalized to automate other unsupervised settings.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 22 of its 63 references that are in the map. First posted on arXiv 1 Nov 2022.

Builds on

22 of 63 references.

References · 22

  1. 2013Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distancescited by 2,277
  2. 2018Computational Optimal Transportcited by 765
  3. 2018Meta-Learning: A Surveycited by 254
  4. 2022ADBench: Anomaly Detection Benchmarkcited by 230
  5. 2019PyOD: A Python Toolbox for Scalable Outlier Detectioncited by 221
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  9. 2022Optimal Transport Tools (OTT): A JAX Toolbox for all things Wassersteincited by 96
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  11. 2021Low-Rank Sinkhorn Factorizationcited by 66
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10 more
  1. 2021Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Datacited by 45
  2. 2019The Information Complexity of Learning Tasks, their Structure and their Distancecited by 35
  3. 2016How to Evaluate the Quality of Unsupervised Anomaly Detection Algorithms?cited by 22
  4. 2020PyODDS: An End-to-end Outlier Detection System with Automated Machine Learningcited by 21
  5. 2022Low-rank Optimal Transport: Approximation, Statistics and Debiasingcited by 21
  6. 2020Learning Autoencoders with Relational Regularizationcited by 20
  7. 2020GAMA: a General Automated Machine learning Assistantcited by 15
  8. 2020An Information-Geometric Distance on the Space of Taskscited by 15
  9. 2021A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?cited by 8
  10. 2022Scrutinizing Shipment Records To Thwart Illegal Timber Tradecited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 3; 2026: 1.

5 papers.

  1. 2025mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scalecited by 6
  2. 2023An Automated Machine Learning Approach for Detecting Anomalous Peak Patterns in Time Series Data from a Research Watershed in the Northeastern United States Critical Zonecited by 1
  3. 2025Reasoning-based Anomaly Detection Framework: A Real-time, Scalable, and Automated Approach to Anomaly Detection Across Domainscited by 0
  4. 2025Automated Machine Learning for Unsupervised Tabular Taskscited by 0
  5. 2026Robustness of AutoML on Dirty Categorical Datacited by 0