Learning hawkes processes under synchronization noise

William Trouleau, Jalal Etesami, Matthias Grossglauser, Negar Kiyavash, Patrick Thiran

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

6 Zitate (Scopus)

Abstract

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.

OriginalspracheEnglisch
Titel36th International Conference on Machine Learning, ICML 2019
Herausgeber (Verlag)International Machine Learning Society (IMLS)
Seiten11022-11031
Seitenumfang10
ISBN (elektronisch)9781510886988
PublikationsstatusVeröffentlicht - 2019
Extern publiziertJa
Veranstaltung36th International Conference on Machine Learning, ICML 2019 - Long Beach, USA/Vereinigte Staaten
Dauer: 9 Juni 201915 Juni 2019

Publikationsreihe

Name36th International Conference on Machine Learning, ICML 2019
Band2019-June

Konferenz

Konferenz36th International Conference on Machine Learning, ICML 2019
Land/GebietUSA/Vereinigte Staaten
OrtLong Beach
Zeitraum9/06/1915/06/19

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