Scalable marked point processes for exchangeable and non-exchangeable event sequences

Aristeidis Panos, Ioannis Kosmidis, Petros Dellaportas
Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, PMLR 206:236-252, 2023.

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.

Cite this Paper

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.} }
Endnote
%0 Conference Paper %T Scalable marked point processes for exchangeable and non-exchangeable event sequences %A Aristeidis Panos %A Ioannis Kosmidis %A Petros Dellaportas %B Proceedings of The 26th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2023 %E Francisco Ruiz %E Jennifer Dy %E Jan-Willem van de Meent %F pmlr-v206-panos23a %I PMLR %P 236--252 %U https://proceedings.mlr.press/v206/panos23a.html %V 206 %X 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.
APA
Panos, A., Kosmidis, I. & Dellaportas, P.. (2023). Scalable marked point processes for exchangeable and non-exchangeable event sequences. Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 206:236-252 Available from https://proceedings.mlr.press/v206/panos23a.html.

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