Probabilistic Modeling for Sequences of Sets in Continuous-Time

arXiv:2312.15045v3 Announce Type: replace
Abstract: Neural marked temporal point processes have been a valuable addition to the existing toolbox of statistical parametric models for continuous-time event data. These models are useful for sequences where each event is associated with a single item (a single type of event or a "mark") -- but such models are not suited for the practical situation where each event is associated with a set of items. In this work, we develop a general framework for …

abstract arxiv continuous cs.lg data event modeling parametric probabilistic modeling processes statistical stat.ml temporal type