RoseCDL: Robust and Scalable Convolutional Dictionary Learning for rare-event and anomaly detection

Jad Yehya, Mansour Benbakoura, Cédric Allain, Benoît Malézieux, Matthieu Kowalski, Thomas Moreau
Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, PMLR 300:982-990, 2026.

Abstract

Detecting rare events and anomalies in large-scale signals is essential in fields such as astronomy, physical simulations, and biomedical science. In many cases, this problem naturally decomposes into identifying common local patterns and detecting deviations that correspond to anomalies. Convolutional Dictionary Learning (CDL) is a powerful tool for modeling local structures, but its adoption for this task has been limited by computational demands and sensitivity to outliers. We introduce RoseCDL, a novel CDL algorithm designed for robust and scalable modeling of signal pattern distribution. RoseCDL leverages stochastic windowing for efficient training and incorporates inline outlier detection to enhance robustness. This enables unsupervised identification of anomalous and rare patterns in long signals based on the local reconstruction loss. Experiments on real-world datasets show that RoseCDL delivers improved detection accuracy and computational efficiency, making CDL practical for challenging detection tasks in large-scale signal analysis.

Cite this Paper

BibTeX
@InProceedings{pmlr-v300-yehya26a, title = { RoseCDL: Robust and Scalable Convolutional Dictionary Learning for rare-event and anomaly detection }, author = {Yehya, Jad and Benbakoura, Mansour and Allain, C{\'e}dric and Mal{\'e}zieux, Beno\^{i}t and Kowalski, Matthieu and Moreau, Thomas}, booktitle = {Proceedings of The 29th International Conference on Artificial Intelligence and Statistics}, pages = {982--990}, year = {2026}, editor = {Khan, Emtiyaz and Li, Yingzhen and Solin, Arno and Ramdas, Aaditya}, volume = {300}, series = {Proceedings of Machine Learning Research}, month = {02--05 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v300/main/assets/yehya26a/yehya26a.pdf}, url = {https://proceedings.mlr.press/v300/yehya26a.html}, abstract = { Detecting rare events and anomalies in large-scale signals is essential in fields such as astronomy, physical simulations, and biomedical science. In many cases, this problem naturally decomposes into identifying common local patterns and detecting deviations that correspond to anomalies. Convolutional Dictionary Learning (CDL) is a powerful tool for modeling local structures, but its adoption for this task has been limited by computational demands and sensitivity to outliers. We introduce RoseCDL, a novel CDL algorithm designed for robust and scalable modeling of signal pattern distribution. RoseCDL leverages stochastic windowing for efficient training and incorporates inline outlier detection to enhance robustness. This enables unsupervised identification of anomalous and rare patterns in long signals based on the local reconstruction loss. Experiments on real-world datasets show that RoseCDL delivers improved detection accuracy and computational efficiency, making CDL practical for challenging detection tasks in large-scale signal analysis. } }
Endnote
%0 Conference Paper %T RoseCDL: Robust and Scalable Convolutional Dictionary Learning for rare-event and anomaly detection %A Jad Yehya %A Mansour Benbakoura %A Cédric Allain %A Benoît Malézieux %A Matthieu Kowalski %A Thomas Moreau %B Proceedings of The 29th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2026 %E Emtiyaz Khan %E Yingzhen Li %E Arno Solin %E Aaditya Ramdas %F pmlr-v300-yehya26a %I PMLR %P 982--990 %U https://proceedings.mlr.press/v300/yehya26a.html %V 300 %X Detecting rare events and anomalies in large-scale signals is essential in fields such as astronomy, physical simulations, and biomedical science. In many cases, this problem naturally decomposes into identifying common local patterns and detecting deviations that correspond to anomalies. Convolutional Dictionary Learning (CDL) is a powerful tool for modeling local structures, but its adoption for this task has been limited by computational demands and sensitivity to outliers. We introduce RoseCDL, a novel CDL algorithm designed for robust and scalable modeling of signal pattern distribution. RoseCDL leverages stochastic windowing for efficient training and incorporates inline outlier detection to enhance robustness. This enables unsupervised identification of anomalous and rare patterns in long signals based on the local reconstruction loss. Experiments on real-world datasets show that RoseCDL delivers improved detection accuracy and computational efficiency, making CDL practical for challenging detection tasks in large-scale signal analysis.
APA
Yehya, J., Benbakoura, M., Allain, C., Malézieux, B., Kowalski, M. & Moreau, T.. (2026). RoseCDL: Robust and Scalable Convolutional Dictionary Learning for rare-event and anomaly detection . Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 300:982-990 Available from https://proceedings.mlr.press/v300/yehya26a.html.

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