Poster
Online Learning for Multivariate Hawkes Processes
Yingxiang Yang · Jalal Etesami · Niao He · Negar Kiyavash
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 $f_{i,j}(t)$ 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 $\calO(\log T)$ 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 the parametric online learning algorithm.
Author Information
Yingxiang Yang (University of Illinois at Urbana Champaign)
PhD student at ECE/UIUC.
Jalal Etesami (UIUC)
Niao He (UIUC)
Negar Kiyavash (Georgia Tech)
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