Scalable Marked Point Processes for Exchangeable and Non-Exchangeable Event Sequences


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 largescale applications is demonstrated through a case study involving all events occurring in an English Premier League season.

In the 26th International Conference on Artificial Intelligence and Statistics
Aristeidis Panos
Aristeidis Panos
Research Associate in Machine Learning