Scalable marked point processes for exchangeable and non-exchangeable event sequences
Aristeidis Panos, Ioannis Kosmidis, Petros Dellaportas
Abstract
We adopt the interpretability offered by a parametric, Hawkes-process-inspired conditional probability mass function for the marks and apply variational inference techniques to derive a general and scalable inferential framework for marked point processes. The framework can handle both exchangeable and non-exchangeable event sequences with minimal tuning and without any pre-training. This contrasts with many parametric and non-parametric state-of-the-art methods that typically require pre-training and/or careful tuning, and can only handle exchangeable event sequences. The framework’s competitive computational and predictive performance against other state-of-the-art methods are illustrated through real data experiments. Its attractiveness for large-scale applications is demonstrated through a case study involving all events occurring in an English Premier League season.
BibTeX
@InProceedings{pmlr-v206-panos23a,
title = {Scalable marked point processes for exchangeable and non-exchangeable event sequences},
author = {Panos, Aristeidis and Kosmidis, Ioannis and Dellaportas, Petros},
booktitle = {Proceedings of The 26th International Conference on Artificial Intelligence and Statistics},
pages = {236--252},
year = {2023},
editor = {Ruiz, Francisco and Dy, Jennifer and van de Meent, Jan-Willem},
volume = {206},
series = {Proceedings of Machine Learning Research},
month = {25--27 Apr},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v206/panos23a/panos23a.pdf},
url = {https://proceedings.mlr.press/v206/panos23a.html},
abstract = {We adopt the interpretability offered by a parametric, Hawkes-process-inspired conditional probability mass function for the marks and apply variational inference techniques to derive a general and scalable inferential framework for marked point processes. The framework can handle both exchangeable and non-exchangeable event sequences with minimal tuning and without any pre-training. This contrasts with many parametric and non-parametric state-of-the-art methods that typically require pre-training and/or careful tuning, and can only handle exchangeable event sequences. The framework’s competitive computational and predictive performance against other state-of-the-art methods are illustrated through real data experiments. Its attractiveness for large-scale applications is demonstrated through a case study involving all events occurring in an English Premier League season.}
}