Wasserstein Learning of Deep Generative Point Process Models
Shuai Xiao, Mehrdad Farajtabar, Xiaojing Ye, Junchi Yan, Le Song, Hongyuan Zha
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.
BibTeX
@inproceedings{NIPS2017_f45a1078,
author = {Xiao, Shuai and Farajtabar, Mehrdad and Ye, Xiaojing and Yan, Junchi and Song, Le and Zha, Hongyuan},
booktitle = {Advances in Neural Information Processing Systems},
editor = {I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Wasserstein Learning of Deep Generative Point Process Models},
url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/f45a1078feb35de77d26b3f7a52ef502-Paper.pdf},
volume = {30},
year = {2017}
}