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Poster
Proximal Graphical Event Models
Debarun Bhattacharjya · Dharmashankar Subramanian · Tian Gao

Wed Dec 05 02:00 PM -- 04:00 PM (PST) @ Room 210 #6
in Wed Poster Session B »

Event datasets include events that occur irregularly over the timeline and are prevalent in numerous domains. We introduce proximal graphical event models (PGEM) as a representation of such datasets. PGEMs belong to a broader family of models that characterize relationships between various types of events, where the rate of occurrence of an event type depends only on whether or not its parents have occurred in the most recent history. The main advantage over the state of the art models is that they are entirely data driven and do not require additional inputs from the user, which can require knowledge of the domain such as choice of basis functions or hyperparameters in graphical event models. We theoretically justify our learning of optimal windows for parental history and the choices of parental sets, and the algorithm are sound and complete in terms of parent structure learning. We present additional efficient heuristics for learning PGEMs from data, demonstrating their effectiveness on synthetic and real datasets.

Keywords: Stochastic Methods · Model Selection and Structure Learning · Graphical Models · Algorithms · Adaptive Data Analysis
[ Paper] 

Author Information

Debarun Bhattacharjya (IBM Research)
Dharmashankar Subramanian (IBM Research)
Tian Gao (IBM Research AI)

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