Multi-Task Multi-Dimensional Hawkes Processes for Modeling Event Sequences
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Special Track on Machine Learning. Pages 3685-3690.
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.
@inproceedings{ijcai2015p518,
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 = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {3685--3690},
year = {2015},
month = {7},
note = {Special Track on Machine Learning},
url = {https://ijcai.org/proceedings/2015/518},
}
TY - CPAPER TI - Multi-Task Multi-Dimensional Hawkes Processes for Modeling Event Sequences AU - Luo, Dixin AU - Xu, Hongteng AU - Zhen, Yi AU - Ning, Xia AU - Zha, Hongyuan AU - Yang, Xiaokang AU - Zhang, Wenjun T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 3685 EP - 3690 UR - https://ijcai.org/proceedings/2015/518 ER -Copied
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