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Poster
Learning to Select Exogenous Events for Marked Temporal Point Process
Ping Zhang · Rishabh Iyer · Ashish Tendulkar · Gaurav Aggarwal · Abir De

Thu Dec 09 04:30 PM -- 06:00 PM (PST) @
in Poster Session 7 »

Marked temporal point processes (MTPPs) have emerged as a powerful modelingtool for a wide variety of applications which are characterized using discreteevents localized in continuous time. In this context, the events are of two typesendogenous events which occur due to the influence of the previous events andexogenous events which occur due to the effect of the externalities. However, inpractice, the events do not come with endogenous or exogenous labels. To thisend, our goal in this paper is to identify the set of exogenous events from a set ofunlabelled events. To do so, we first formulate the parameter estimation problemin conjunction with exogenous event set selection problem and show that thisproblem is NP hard. Next, we prove that the underlying objective is a monotoneand \alpha-submodular set function, with respect to the candidate set of exogenousevents. Such a characterization subsequently allows us to use a stochastic greedyalgorithm which was originally proposed in~\cite{greedy}for submodular maximization.However, we show that it also admits an approximation guarantee for maximizing\alpha-submodular set function, even when the learning algorithm provides an imperfectestimates of the trained parameters. Finally, our experiments with synthetic andreal data show that our method performs better than the existing approaches builtupon superposition of endogenous and exogenous MTPPs.

Keywords:

Author Information

Ping Zhang (University of Texas at Dallas)
Rishabh Iyer (University of Texas, Dallas)

Bio: Prof. Rishabh Iyer is currently an Assistant Professor at the University of Texas, Dallas, where he leads the CARAML Lab. He is also a Visiting Assistant Professor at the Indian Institute of Technology, Bombay. He completed his Ph.D. in 2015 from the University of Washington, Seattle. He is excited in making ML more efficient (both computational and labeling efficiency), robust, and fair. He has received the best paper award at Neural Information Processing Systems (NeurIPS/NIPS) in 2013, the International Conference of Machine Learning (ICML) in 2013, and an Honorable Mention at CODS-COMAD in 2021. He has also won a Microsoft Research Ph.D. Fellowship, a Facebook Ph.D. Fellowship, and the Yang Award for Outstanding Graduate Student from the University of Washington.

Ashish Tendulkar (Google)
Gaurav Aggarwal (Google)
Abir De (IIT Bombay)

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