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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.
Author Information
Renbo Zhao (MIT)
Niccolo Dalmasso (Carnegie Mellon University)
Mohsen Ghassemi (J.P. Morgan AI research)
Vamsi Potluru (J.P. Morgan AI Research)
Tucker Balch (J.P. Morgan)
Tucker Balch is a managing director at J.P. Morgan AI Research. He is a professor on leave from Georgia Tech. He is interested in research problems concerning multi agent social behavior. This interest has led to research in a wide range of topics from financial markets to to tracking and modeling the behavior of ants, honeybees and monkeys. He teaches courses in Robotics, Machine Learning and Finance. In addition to his teaching on campus, more than 170,000 students have take his courses online via Coursera and Udacity. He is Chief Scientist and co-founder of Lucena Research, an investment software firm that applies Machine Learning and Big Data approaches to investment problems. Balch has published 120 conference papers and journal articles. His work has been covered by CNN, New Scientist, Institutional Investor, and the New York Times. His graduated students work at NASA/JPL, Boston Dynamics, Goldman Sachs, Morgan Stanley, Citadel, AQR, and Yahoo! Finance. Before his career in academia, Balch was a USAF F-15 pilot.
Manuela Veloso (JPMorgan and Carnegie Mellon University)
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