Multi-Task Multi-Dimensional Hawkes Processes for Modeling Event Sequences
Abstract
We propose a Multi-task Multi-dimensional Hawkes Process (MMHP) for modeling event sequences where there exist multiple triggering patterns within sequences and structures across sequences. MMHP is able to model the dynamics of multiple sequences jointly by imposing structural constraints and thus systematically uncover clustering structure among sequences. We propose an effective and robust optimization algorithm to learn MMHP models, which takes advantage of alternating direction method of multipliers (ADMM), majorization minimization and Euler-Lagrange equations. Our experimental results demonstrate that MMHP performs well on both synthetic and real data.
Cite
Text
Luo et al. "Multi-Task Multi-Dimensional Hawkes Processes for Modeling Event Sequences." International Joint Conference on Artificial Intelligence, 2015.Markdown
[Luo et al. "Multi-Task Multi-Dimensional Hawkes Processes for Modeling Event Sequences." International Joint Conference on Artificial Intelligence, 2015.](https://mlanthology.org/ijcai/2015/luo2015ijcai-multi/)BibTeX
@inproceedings{luo2015ijcai-multi,
title = {{Multi-Task Multi-Dimensional Hawkes Processes for Modeling Event Sequences}},
author = {Luo, Dixin and Xu, Hongteng and Zhen, Yi and Ning, Xia and Zha, Hongyuan and Yang, Xiaokang and Zhang, Wenjun},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2015},
pages = {3685-3691},
url = {https://mlanthology.org/ijcai/2015/luo2015ijcai-multi/}
}