Title: Wasserstein Learning of Deep Generative Point Process Models,
Abstract: Point processes are becoming very popular in modeling asynchronous sequential
data due to their sound mathematical foundation and strength in modeling a
variety of real-world phenomena. Currently, they are often characterized via
intensity function which limits model's expressiveness due to unrealistic
assumptions on its parametric form used in practice. Furthermore, they are
learned via maximum likelihood approach which is prone to failure in
multi-modal distributions of sequences. In this paper, we propose an
intensity-free approach for point processes modeling that transforms nuisance
processes to a target one. Furthermore, we train the model using a
likelihood-free leveraging Wasserstein distance between point processes.
Experiments on various synthetic and real-world data substantiate the
superiority of the proposed point process model over conventional ones.