Luo, DixinXu, HongtengZhen, YiNing, XiaZha, HongyuanYang, XiaokangZhang, Wenjun2016-06-232016-06-232015-07Luo, D., Xu, H., Zhen, Y., Ning, X., Zha, H., Yang, X., & Zhang, W. (2015, July). Multi-task multi-dimensional hawkes processes for modeling event sequences. In Proceedings of the 24th International Conference on Artificial Intelligence (pp. 3685-3691). AAAI Press.https://hdl.handle.net/1805/10142We 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.enPublisher PolicyMulti-task Multi-dimensional Hawkes Process (MMHP)event sequence modelingArticle