UNHaP: Unmixing Noise from Hawkes Processes

Virginie Loison, Guillaume Staerman, Thomas Moreau
Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, PMLR 258:1342-1350, 2025.

Abstract

Physiological signal analysis often involves identifying events crucial to understanding biological dynamics. Many methods have been proposed to detect them, from handcrafted and supervised approaches to unsupervised techniques. All these methods tend to produce spurious events, mainly as they detect each event independently. This work introduces UNHaP (Unmix Noise from Hawkes Processes), a novel approach addressing the joint learning of temporal structures in events and the removal of spurious detections. By treating the event detection output as a mixture of structured Hawkes and unstructured Poisson events, UNHaP efficiently unmixes these processes and estimates their parameters. This approach significantly enhances event distribution characterization while minimizing false detection rates on simulated and real data.

Cite this Paper

BibTeX
@InProceedings{pmlr-v258-loison25a, title = {UNHaP: Unmixing Noise from Hawkes Processes}, author = {Loison, Virginie and Staerman, Guillaume and Moreau, Thomas}, booktitle = {Proceedings of The 28th International Conference on Artificial Intelligence and Statistics}, pages = {1342--1350}, 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/loison25a/loison25a.pdf}, url = {https://proceedings.mlr.press/v258/loison25a.html}, abstract = {Physiological signal analysis often involves identifying events crucial to understanding biological dynamics. Many methods have been proposed to detect them, from handcrafted and supervised approaches to unsupervised techniques. All these methods tend to produce spurious events, mainly as they detect each event independently. This work introduces UNHaP (Unmix Noise from Hawkes Processes), a novel approach addressing the joint learning of temporal structures in events and the removal of spurious detections. By treating the event detection output as a mixture of structured Hawkes and unstructured Poisson events, UNHaP efficiently unmixes these processes and estimates their parameters. This approach significantly enhances event distribution characterization while minimizing false detection rates on simulated and real data.} }
Endnote
%0 Conference Paper %T UNHaP: Unmixing Noise from Hawkes Processes %A Virginie Loison %A Guillaume Staerman %A Thomas Moreau %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-loison25a %I PMLR %P 1342--1350 %U https://proceedings.mlr.press/v258/loison25a.html %V 258 %X Physiological signal analysis often involves identifying events crucial to understanding biological dynamics. Many methods have been proposed to detect them, from handcrafted and supervised approaches to unsupervised techniques. All these methods tend to produce spurious events, mainly as they detect each event independently. This work introduces UNHaP (Unmix Noise from Hawkes Processes), a novel approach addressing the joint learning of temporal structures in events and the removal of spurious detections. By treating the event detection output as a mixture of structured Hawkes and unstructured Poisson events, UNHaP efficiently unmixes these processes and estimates their parameters. This approach significantly enhances event distribution characterization while minimizing false detection rates on simulated and real data.
APA
Loison, V., Staerman, G. & Moreau, T.. (2025). UNHaP: Unmixing Noise from Hawkes Processes. Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 258:1342-1350 Available from https://proceedings.mlr.press/v258/loison25a.html.

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