Workshop: Synthetic Data for Empowering ML Research

Fast Learning of Multidimensional Hawkes Processes via Frank-Wolfe

Renbo Zhao · Niccolo Dalmasso · Mohsen Ghassemi · Vamsi Potluru · Tucker Balch · Manuela Veloso

[ Abstract ] [ Website ]
[ Poster [ OpenReview
Fri 2 Dec 8:34 a.m. PST — 8:36 a.m. PST


Hawkes processes have recently risen to the forefront of tools when it comes to modeling and generating sequential events data. Multidimensional Hawkes processes model both the self and cross-excitation between different types of events and have been applied successfully in various domain such as finance, epidemiology and personalized recommendations, among others. In this work we present an adaptation of the Frank-Wolfe algorithm for learning multidimensional Hawkes processes. Experimental results show that our approach has better or on par accuracy in terms of parameter estimation than other first order methods, while enjoying a significantly faster runtime.

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