Benefits from Superposed Hawkes Processes
Publication
, Conference
Xu, H; Luo, D; Chen, X; Carin, L
Published in: Proceedings of Machine Learning Research
January 1, 2018
The superposition of temporal point processes has been studied for many years, although the usefulness of such models for practical applications has not be fully developed. We investigate superposed Hawkes process as an important class of such models, with properties studied in the framework of least squares estimation. The superposition of Hawkes processes is demonstrated to be beneficial for tightening the upper bound of excess risk under certain conditions, and we show the feasibility of the benefit in typical situations. The usefulness of superposed Hawkes processes is verified on synthetic data, and its potential to solve the cold-start problem of recommendation systems is demonstrated on real-world data.
Duke Scholars
Published In
Proceedings of Machine Learning Research
EISSN
2640-3498
Publication Date
January 1, 2018
Volume
84
Citation
APA
Chicago
ICMJE
MLA
NLM
Xu, H., Luo, D., Chen, X., & Carin, L. (2018). Benefits from Superposed Hawkes Processes. In Proceedings of Machine Learning Research (Vol. 84).
Xu, H., D. Luo, X. Chen, and L. Carin. “Benefits from Superposed Hawkes Processes.” In Proceedings of Machine Learning Research, Vol. 84, 2018.
Xu H, Luo D, Chen X, Carin L. Benefits from Superposed Hawkes Processes. In: Proceedings of Machine Learning Research. 2018.
Xu, H., et al. “Benefits from Superposed Hawkes Processes.” Proceedings of Machine Learning Research, vol. 84, 2018.
Xu H, Luo D, Chen X, Carin L. Benefits from Superposed Hawkes Processes. Proceedings of Machine Learning Research. 2018.
Published In
Proceedings of Machine Learning Research
EISSN
2640-3498
Publication Date
January 1, 2018
Volume
84