Transfer Neyman-Pearson Algorithm for Outlier Detection

Mohammadreza Mousavi Kalan, Eitan J. Neugut, Samory Kpotufe
Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, PMLR 258:4717-4725, 2025.

Abstract

We consider the problem of transfer learning in outlier detection where target abnormal data is rare. While transfer learning has been considered extensively in traditional classification, the problem of transfer in outlier detection and more generally in imbalanced classification settings has received less attention. We propose a general algorithmic approach which is shown theoretically to yield strong guarantees w.r.t. to a range of changes in abnormal distribution, and at the same time amenable to practical implementation. We then investigate different instantiations of this general algorithmic approach, e.g., based on multi-layer neural networks, and show empirically that they significantly outperform natural extensions of transfer methods from traditional classification (which are the only solutions available at the moment)

Cite this Paper

BibTeX
@InProceedings{pmlr-v258-kalan25a, title = {Transfer Neyman-Pearson Algorithm for Outlier Detection}, author = {Kalan, Mohammadreza Mousavi and Neugut, Eitan J. and Kpotufe, Samory}, booktitle = {Proceedings of The 28th International Conference on Artificial Intelligence and Statistics}, pages = {4717--4725}, year = {2025}, editor = {Li, Yingzhen and Mandt, Stephan and Agrawal, Shipra and Khan, Emtiyaz}, volume = {258}, series = {Proceedings of Machine Learning Research}, month = {03--05 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v258/main/assets/kalan25a/kalan25a.pdf}, url = {https://proceedings.mlr.press/v258/kalan25a.html}, abstract = {We consider the problem of transfer learning in outlier detection where target abnormal data is rare. While transfer learning has been considered extensively in traditional classification, the problem of transfer in outlier detection and more generally in imbalanced classification settings has received less attention. We propose a general algorithmic approach which is shown theoretically to yield strong guarantees w.r.t. to a range of changes in abnormal distribution, and at the same time amenable to practical implementation. We then investigate different instantiations of this general algorithmic approach, e.g., based on multi-layer neural networks, and show empirically that they significantly outperform natural extensions of transfer methods from traditional classification (which are the only solutions available at the moment)} }
Endnote
%0 Conference Paper %T Transfer Neyman-Pearson Algorithm for Outlier Detection %A Mohammadreza Mousavi Kalan %A Eitan J. Neugut %A Samory Kpotufe %B Proceedings of The 28th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2025 %E Yingzhen Li %E Stephan Mandt %E Shipra Agrawal %E Emtiyaz Khan %F pmlr-v258-kalan25a %I PMLR %P 4717--4725 %U https://proceedings.mlr.press/v258/kalan25a.html %V 258 %X We consider the problem of transfer learning in outlier detection where target abnormal data is rare. While transfer learning has been considered extensively in traditional classification, the problem of transfer in outlier detection and more generally in imbalanced classification settings has received less attention. We propose a general algorithmic approach which is shown theoretically to yield strong guarantees w.r.t. to a range of changes in abnormal distribution, and at the same time amenable to practical implementation. We then investigate different instantiations of this general algorithmic approach, e.g., based on multi-layer neural networks, and show empirically that they significantly outperform natural extensions of transfer methods from traditional classification (which are the only solutions available at the moment)
APA
Kalan, M.M., Neugut, E.J. & Kpotufe, S.. (2025). Transfer Neyman-Pearson Algorithm for Outlier Detection. Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 258:4717-4725 Available from https://proceedings.mlr.press/v258/kalan25a.html.

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