Reviews: Stochastic Nonparametric Event-Tensor Decomposition

The work proposed a nonparametric Bayesian model for event tensor decomposition. Existing tensor works lack of a way to integrate the complete temporal information in the factorization. This work formulates the so called "event-tensor" to preserver all the time stamps, where each entry consists of a sequence of events rather than a value. To decompose the event-tensor, the work hybridizes Gaussian processes and Hawkes processes to model the entries as mutually excited Hawkes processes, and base rates Gaussian processes on the latent factors. The authors exploit the Poisson process super-position theorem and variational sparse GP framework to derive a decomposable variational lower bound and develop a doubly stochastic algorithm for scalable inference.