# Unmixing Noise from Hawkes Process to Model Learned Physiological Events

arXiv: https://arxiv.org/abs/2406.16938 · HTML: https://mlsanity.com/paper/2406.16938/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 39 references that are in the map.
- First posted on arXiv 17 Jun 2024.

## Builds on (most cited first, 5 of 5)

- [Fast and Flexible Temporal Point Processes with Triangular Maps](https://mlsanity.com/paper/2006.12631/) (2020)
- [A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering](https://mlsanity.com/paper/1701.09177/) (2017)
- [Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals](https://mlsanity.com/paper/1805.09654/) (2018)
- [FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels](https://mlsanity.com/paper/2210.04635/) (2022)
- [DriPP: Driven Point Processes to Model Stimuli Induced Patterns in M/EEG Signals](https://mlsanity.com/paper/2112.06652/) (2021)

## Built on by (most cited first, 1 of 1)

- [Learning under Commission and Omission Event Outliers](https://mlsanity.com/paper/2501.13599/) (2025)

## Abstract (arXiv)

Physiological signal analysis often involves identifying events crucial to understanding biological dynamics. Traditional methods rely on handcrafted procedures or supervised learning, presenting challenges such as expert dependence, lack of robustness, and the need for extensive labeled data. Data-driven methods like Convolutional Dictionary Learning (CDL) offer an alternative but tend to produce spurious detections. 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. Leveraging marked Hawkes processes, UNHaP distinguishes between events of interest and spurious ones. By treating the event detection output as a mixture of structured and unstructured events, UNHaP efficiently unmixes these processes and estimates their parameters. This approach significantly enhances the understanding of event distributions while minimizing false detection rates.