research-article

Decoupled Learning for Factorial Marked Temporal Point Processes

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Hongyuan ZhaAuthors Info & Claims

KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 2516 - 2525

https://doi.org/10.1145/3219819.3220035

Published: 19 July 2018 Publication History

Abstract

This paper presents a factorial marked temporal point process model and presents efficient learning methods. In conventional (multi-dimensional) marked temporal point process models, an event is often encoded by a single discrete variable (marker). We describe the factorial marked point processes whereby time-stamped event is factored into multiple markers. Accordingly the size of the infectivity matrix modeling the effect between pairwise markers is in exponential order regarding the number of discrete markers. We propose a decoupled learning method with two learning procedures: i) directly solving the model based on two techniques: Alternating Direction Method of Multipliers and Fast Iterative Shrinkage-Thresholding Algorithm; ii) involving a reformulation that transforms the original problem into a Logistic Regression model for more efficient learning. Moreover, a sparse group regularizer is added to identify the key profile features and event labels. Empirical results on real world datasets demonstrate the efficiency of our decoupled and reformulated method.

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References

[1]

Amir Beck and Marc Teboulle . 2009. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM journal on imaging sciences Vol. 2, 1 (2009), 183--202.

Digital Library

[2]

D. Daley and D. Vere-Jones . 2007. An introduction to the theory of point processes: volume II: general theory and structure. Springer Science & Business Media.

[3]

David L Donoho . 1995. De-noising by soft-thresholding. IEEE Transactions on Information Theory Vol. 41, 3 (1995), 613--627.

Digital Library

[4]

Nan Du, Hanjun Dai, Rakshit Trivedi, Utkarsh Upadhyay, Manuel Gomez-Rodriguez, and Le Song . 2016. Recurrent marked temporal point processes: Embedding event history to vector SIGKDD. ACM.

Digital Library

[5]

Nan Du, Yichen Wang, Niao He, Jimeng Sun, and Le Song . 2015. Time-sensitive recommendation from recurrent user activities NIPS.

Digital Library

[6]

Alan E Gelfand, Peter Diggle, Peter Guttorp, and Montserrat Fuentes . 2010. Handbook of spatial statistics. CRC press.

[7]

Zoubin Ghahramani and Michael I Jordan . 1996. Factorial hidden Markov models. In Advances in Neural Information Processing Systems. 472--478.

Digital Library

[8]

Ary L. Goldberger, Luis A.N. Amaral, Leon Glass, Jeffrey M. Hausdorff, Plamen Ch. Ivanov, Roger G. Mark, Joseph E. Mietus, George B. Moody, Chung-Kang Peng, and H. Eugene Stanley . 2000. PhysioBank, PhysioToolkit, and PhysioNet: Component of a New Research Resource for Complex Physiologic Signals. Circulation Vol. 101, 23 (2000), e215--e220.

[9]

Alan G Hawkes . 1971. Spectra of some self-exciting and mutually exciting point processes. Biometrika Vol. 58, 1 (1971), 83--90.

[10]

Valerie Isham and Mark Westcott . 1979. A self-correcting point process. Stochastic Processes and Their Applications Vol. 8, 3 (1979), 335--347.

[11]

Erik Lewis and George Mohler . 2011. A nonparametric EM algorithm for multiscale Hawkes processes. Journal of Nonparametric Statistics Vol. 1, 1 (2011), 1--20.

[12]

Liangda Li and Hongyuan Zha . 2014. Learning Parametric Models for Social Infectivity in Multi-Dimensional Hawkes Processes AAAI. 101--107.

Digital Library

[13]

Thomas Josef Liniger . 2009. Multivariate hawkes processes. Ph.D. Dissertation. bibinfoschoolETH Zurich.

[14]

Jun Liu, Shuiwang Ji, and Jieping Ye . 2009. SLEP: Sparse Learning with Efficient Projections. Arizona State University.deftempurl%http://www.public.asu.edu/ jye02/Software/SLEP tempurl

[15]

Chris Lloyd, Tom Gunter, Michael Osborne, and Stephen Roberts . 2015. Variational inference for Gaussian process modulated Poisson processes International Conference on Machine Learning. 1814--1822.

Digital Library

[16]

Lukas Meier, Sara Van De Geer, and Peter Bühlmann . 2008. The group lasso for logistic regression. Journal of the Royal Statistical Society: Series B (Statistical Methodology) Vol. 70, 1 (2008), 53--71.

[17]

Andrew Y Ng . 2004. Feature selection, L 1 vs. L 2 regularization, and rotational invariance ICML.

Digital Library

[18]

Yosihiko Ogata . 1988. Statistical models for earthquake occurrences and residual analysis for point processes. Journal of the American Statistical association Vol. 83, 401 (1988), 9--27.

[19]

Yosihiko Ogata . 1998. Space-time point-process models for earthquake occurrences. Annals of the Institute of Statistical Mathematics Vol. 50, 2 (1998), 379--402.

[20]

Tohru Ozaki . 1979. Maximum likelihood estimation of Hawkes' self-exciting point processes. Annals of the Institute of Statistical Mathematics Vol. 31, 1 (1979), 145--155.

[21]

Noah Simon, Jerome Friedman, Trevor Hastie, and Robert Tibshirani . 2013. A sparse-group lasso. Journal of Computational and Graphical Statistics Vol. 22, 2 (2013), 231--245.

[22]

Shuai Xiao, Junchi Yan, Xiaokang Yang, Hongyuan Zha, and Stephen M Chu . 2017. Modeling the Intensity Function of Point Process Via Recurrent Neural Networks. AAAI. 1597--1603.

[23]

Hongteng Xu, Weichang Wu, Shamim Nemati, and Hongyuan Zha . 2017. Patient flow prediction via discriminative learning of mutually-correcting processes. IEEE Transactions on Knowledge and Data Engineering Vol. 29, 1 (2017), 157--171.

Digital Library

[24]

Junchi Yan, Yu Wang, Ke Zhou, Jin Huang, Chunhua Tian, Hongyuan Zha, and Weishan Dong . 2013. Towards Effective Prioritizing Water Pipe Replacement and Rehabilitation. IJCAI. 2931--2937.

Digital Library

[25]

Ke Zhou, Hongyuan Zha, and Le Song . 2013 a. Learning social infectivity in sparse low-rank networks using multi-dimensional hawkes processes. In AISTATS.

[26]

Ke Zhou, Hongyuan Zha, and Le Song . 2013 b. Learning triggering kernels for multi-dimensional hawkes processes ICML.

Digital Library

Cited By

Wang HZhang QWu YJin DWang XZhu LYu L(2024)Synthesizing Human Trajectories Based on Variational Point ProcessesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.331220936:4(1785-1799)Online publication date: Apr-2024
https://doi.org/10.1109/TKDE.2023.3312209
Wu WZhang XZhao SFu CZhou J(2024)Multi-Task Decouple Learning With Hierarchical Attentive Point ProcessIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.330562836:4(1741-1757)Online publication date: Apr-2024
https://doi.org/10.1109/TKDE.2023.3305628
Long QWang HLi THuang LWang KWu QLi GLiang YYu LLi YSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Practical Synthetic Human Trajectories Generation Based on Variational Point ProcessesProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599888(4561-4571)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599888
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Decoupled Learning for Factorial Marked Temporal Point Processes

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KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

July 2018

2925 pages

ISBN:9781450355520

DOI:10.1145/3219819

General Chairs:
Yike Guo
Imperial College London
,
Faisal Farooq
IBM

Copyright © 2018 ACM.

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Published: 19 July 2018

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NSFC
Partnership Collaboration Awards by The University of Sydney and Shanghai Jiao Tong University
NSFCZhejiang Joint Fund for the Integration of Industrialization and Informatization
The National Key Research and Development Program of China

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KDD '18

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KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 19 - 23, 2018

London, United Kingdom

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KDD '18 Paper Acceptance Rate 107 of 983 submissions, 11%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

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https://doi.org/10.1109/TKDE.2023.3312209
Wu WZhang XZhao SFu CZhou J(2024)Multi-Task Decouple Learning With Hierarchical Attentive Point ProcessIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.330562836:4(1741-1757)Online publication date: Apr-2024
https://doi.org/10.1109/TKDE.2023.3305628
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https://dl.acm.org/doi/10.1145/3580305.3599888
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