Timezone: »

 
Poster
Online Learning for Multivariate Hawkes Processes
Yingxiang Yang · Jalal Etesami · Niao He · Negar Kiyavash

Wed Dec 06 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #49
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)
Jalal Etesami (UIUC)
Niao He (UIUC)
Negar Kiyavash (Georgia Tech)

More from the Same Authors