Awesome Machine Learning for Temporal Point Processes Resources

We would like to maintain a list of resources that utilize machine learning technologies to model temporal point processes.

We mark work contributed by Thinklab with ✨.

Maintained by members in SJTU-Thinklab: Mingquan Feng, Yunhao Zhang, Liangliang Shi and Junchi Yan.

We are looking for post-docs interested in machine learning especially for learning combinatorial solvers, dynamic graphs, and reinforcement learning. Please send your up-to-date resume via yanjunchi AT sjtu.edu.cn.

Content

• 1. Survey Papers
• 2. Deep TPP
  • 2.1 RNN and Transformer
  • 2.2 ODE and SDE
• 3. Traditional TPP
• 4. Applications

1. Survey Papers

1. A review of self-exciting spatio-temporal point processes and their applications JSTOR, 2018. journal

   Reinhart, Alex.

2. ✨Recent advance in temporal point process: from machine learning perspective SJTU, 2019. paper

   Yan, Junchi

3. Neural temporal point processes: A review Arxiv, 2021. paper

   Shchur, Oleksandr, Ali Caner Türkmen, Tim Januschowski, and Stephan Günnemann

2. Deep TPP

2.1 RNN and Transformer

1. Deep Reinforcement Learning of Marked Temporal Point Processes NIPS, 2018. paper

   Utkarsh Upadhyay, Abir De, Manuel Gomez-Rodriguez

2.2 ODE and SDE

1. Neural Spatio-Temporal Point Processes ICLR, 2021. paper, code

   Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel

2. Latent ODEs for Irregularly-Sampled Time Series NeurIPS, 2019. paper, code

   Yulia Rubanova, Ricky T. Q. Chen, David Duvenaud

3. Neural Jump Stochastic Differential Equations NeurIPS, 2019. paper, code

   Junteng Jia, Austin R. Benson

4. Hawkes Processes with Stochastic Excitations ICML, 2016. paper

   Young Lee, Kar Wai Lim, Cheng Soon Ong

3. Traditional TPP

1. Learning Social Infectivity in Sparse Low-rank Networks Using Multi-dimensional Hawkes Processes AISTATS, 2013. paper, code

   Ke Zhou, Hongyuan Zha, and Le Song

2. Learning Granger Causality for Hawkes Processes ICML, 2016. paper, code

   Hongteng Xu, Mehrdad Farajtabar, Hongyuan Zha

3. A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering NIPS, 2017. paper

   Hongteng Xu, Hongyuan Zha

4. Learning Hawkes Processes from Short Doubly-Censored Event Sequences ICML, 2017. paper

   Hongteng Xu, Dixin Luo, Hongyuan Zha

5. Decoupled Learning for Factorial Marked Temporal Point Processes SIGKDD, 2018. paper

   Weichang Wu, Junchi Yan, Xiaokang Yang, Hongyuan Zha

4. Applications

1. Learning Parametric Models for Social Infectivity in Multi-Dimensional Hawkes Processes AAAI, 2014. paper

   Liangda Li, Hongyuan Zha

2. SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity KDD, 2015. paper

   Qingyuan Zhao, Murat A. Erdogdu, Hera Y. He, Anand Rajaraman, Jure Leskovec

3. Trailer Generation via a Point Process-Based Visual Attractiveness Model IJCAI, 2015. paper

   Hongteng Xu, Yi Zhen, Hongyuan Zha

4. On Machine Learning towards Predictive Sales Pipeline Analytics AAAI, 2015. paper

   Junchi Yan, Chao Zhang, Hongyuan Zha, Min Gong, Changhua Sun, Jin Huang, S. Chu, Xiaokang Yang

5. PInfer: Learning to Infer Concurrent Request Paths from System Kernel Events ICAC, 2016. paper

   Hongteng Xu, Xia Ning, Hui Zhang, Junghwan Rhee, Guofei Jiang

6. Modeling Contagious Merger and Acquisition via Point Processes with a Profile Regression Prior IJCAI, 2016. paper

   Junchi Yan, Shuai Xiao, Changsheng Li, Bo Jin, Xiangfeng Wang, Bin Ke, Xiaokang Yang, Hongyuan Zha

7. Patient Flow Prediction via Discriminative Learning of Mutually-Correcting Processes TKDE, 2016. paper

   Hongteng Xu , Weichang Wu , Shamim Nemati , Hongyuan Zha

8. Expecting to be HIP: Hawkes Intensity Processes for Social Media Popularity WWW, 2017. paper

   Marian-Andrei Rizoiu, Lexing Xie, Scott Sanner, Manuel Cebrian, Honglin Yu, Pascal Van Hentenryck

9. On Predictive Patent Valuation: Forecasting Patent Citations and Their Types AAAI, 2017. paper

   Xin Liu, Junchi Yan, Shuai Xiao, Xiangfeng Wang, H. Zha, S. Chu

10. LMPP: A Large Margin Point Process Combining Reinforcement and Competition for Modeling Hashtag Popularity IJCAI, 2017. paper

    Bidisha Samanta, A. De, Abhijnan Chakraborty, Niloy Ganguly

11. Shaping Opinion Dynamics in Social Networks AAMAS, 2018. paper

    Abir De, Sourangshu Bhattacharya, Niloy Ganguly

12. Adversarial Training Model Unifying Feature Driven and Point Process Perspectives for Event Popularity Prediction CIKM, 2018. paper

    Qitian Wu, Chaoqi Yang, Hengrui Zhang, Xiaofeng Gao, Paul Weng, Guihai Chen

13. CRPP: Competing Recurrent Point Process for Modeling Visibility Dynamics in Information Diffusion CIKM, 2018. paper

    Avirup Saha, Bidisha Samanta, Niloy Ganguly, Abir De

14. Recurrent Spatio-Temporal Point Process for Check-in Time Prediction CIKM, 2018. paper

    *Guolei Yang, Ying Cai, Chandan K. Reddy *

15. Modeling Sequential Online Interactive Behaviors with Temporal Point Process CIKM, 2018. paper

    *Renqin Cai, Xueying Bai, Zhenrui Wang, Yuling Shi, Parikshit Sondhi, Hongning Wang *

16. INITIATOR: Noise-contrastive Estimation for Marked Temporal Point Process IJCAI, 2018. paper

    Ruocheng Guo, Jundong Li, Huan Liu

17. Learning Network Traffic Dynamics Using Temporal Point Process IEEE INFOCOM 2019. paper

    Avirup Saha; Niloy Ganguly; Sandip Chakraborty; Abir De

18. Understanding species distribution in dynamic populations: a new approach using spatio-temporal point process models Ecography, 2019, 42(6): 1092-1102. journal

    Andrea Soriano-Redondo, Charlotte M. Jones-Todd, Stuart Bearhop, Geoff M. Hilton, Leigh Lock, Andrew Stanbury, Stephen C. Votier and Janine B. Illian

19. Modeling Event Propagation via Graph Biased Temporal Point Process IEEE Transactions on Neural Networks and Learning Systems, 2020. paper

    Weichang Wu; Huanxi Liu; Xiaohu Zhang; Yu Liu; Hongyuan Zha

20. VigDet: Knowledge Informed Neural Temporal Point Process for Coordination Detection on Social Media NeurIPS 2021. paper

    Yizhou Zhang, Karishma Sharma, Yan Liu