Proceedings

Multi-Task Multi-Dimensional Hawkes Processes for Modeling Event Sequences

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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.

Dixin Luo, Hongteng Xu, Yi Zhen, Xia Ning, Hongyuan Zha, Xiaokang Yang, and Wenjun Zhang. 2015. Multi-Task Multi-Dimensional Hawkes Processes for Modeling Event Sequences. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15. IJCAI, 3685–3690. https://ijcai.org/proceedings/2015/518
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