Online learning for multivariate hawkes processes

Yingxiang Yang, Jalal Etesami, Niao He, Negar Kiyavash

Research output: Contribution to journalConference article

Abstract

We develop a nonparametric and online learning algorithm that estimates the triggering functions of a multivariate Hawkes process (MHP). The approach we take approximates the triggering function fi,j(t) by functions in a reproducing kernel Hilbert space (RKHS), and maximizes a time-discretized version of the log-likelihood, with Tikhonov regularization. Theoretically, our algorithm achieves an O(log T) regret bound. Numerical results show that our algorithm offers a competing performance to that of the nonparametric batch learning algorithm, with a run time comparable to parametric online learning algorithms.

Original languageEnglish (US)
Pages (from-to)4938-4947
Number of pages10
JournalAdvances in Neural Information Processing Systems
Volume2017-December
StatePublished - Jan 1 2017
Event31st Annual Conference on Neural Information Processing Systems, NIPS 2017 - Long Beach, United States
Duration: Dec 4 2017Dec 9 2017

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ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

Cite this

Yang, Y., Etesami, J., He, N., & Kiyavash, N. (2017). Online learning for multivariate hawkes processes. Advances in Neural Information Processing Systems, 2017-December, 4938-4947.