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Learning Hawkes Processes Under Synchronization Noise
William Trouleau · Jalal Etesami · Matthias Grossglauser · Negar Kiyavash · Patrick Thiran

Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #251

Multivariate Hawkes processes (MHP) are widely used in a variety of fields to model the occurrence of discrete events. Prior work on learning MHPs has only focused on inference in the presence of perfect traces without noise. We address the problem of learning the causal structure of MHPs when observations are subject to an unknown delay. In particular, we introduce the so-called synchronization noise, where the stream of events generated by each dimension is subject to a random and unknown time shift. We characterize the robustness of the classic maximum likelihood estimator to synchronization noise, and we introduce a new approach for learning the causal structure in the presence of noise. Our experimental results show that our approach accurately recovers the causal structure of MHPs for a wide range of noise levels, and significantly outperforms classic estimation methods.

Author Information

William Trouleau (EPFL)
Jalal Etesami (Bosch Research Center for AI, Germany)
Matthias Grossglauser (EPFL)

Matthias Grossglauser is Associate Professor in the School of Computer and Communication Sciences at EPFL. His current research interests are in machine learning for large social systems, stochastic models and algorithms for graph and mobility mining, and recommender systems. He is also the current director of the Doctoral School in Computer and Communication Sciences. From 2007-2010, he was with the Nokia Research Center (NRC) in Helsinki, Finland, serving as head of the Internet Laboratory, and later as head of a tech-transfer program focused on data mining, analytics, and machine learning. In addition, he served on Nokia's CEO Technology Council, a technology advisory group reporting to the CEO. Prior to this, he was Assistant Professor at EPFL, and a Research Scientist in the Networking and Distributed Systems Laboratory at AT&T Research in New Jersey. He received the 1998 Cor Baayen Award from the European Research Consortium for Informatics and Mathematics (ERCIM), the 2006 CoNEXT/SIGCOMM Rising Star Award, and two best paper awards. He served on the editorial board of IEEE/ACM Transactions on Networking, and on numerous Technical Program Committees.

Negar Kiyavash (Georgia Institute of Technology)
Patrick Thiran (EPFL)

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