Revisiting the Mark Conditional Independence Assumption in Neural Marked Temporal Point Processes

Learning marked temporal point process (TPP) models involves modeling both the event arrival times as well as their associated labels, referred to as marks. The recent introduction of deep learning techniques to the field led to better modeling of event sequences thanks to more flexible neural TPP models. However, some of these models make the assumption that event marks are independent of event times given the history of the process, which may not be valid in many applications. We
relax this assumption and explicitly parametrize the mark distribution as
a function of the current event time. We show that our approach achieves
improved performance in predicting future marks compared to baselines
on multiple real-world event sequence datasets, without affecting the performance on event time prediction.