Modeling and Applications for Temporal Point Processes
A tutorial at KDD 2019, August 4 - 8, 2019, Anchorage, Alaska, USA
ID: T14, Time: 8:00am-11:00am August 4, Location: Summit 8-Ground Level, Egan


Real-world entities’ behaviors, associated with their side information, are often recorded over time as asynchronous event sequences. Such event sequences are the basis of many practical applications, neural spiking train study, earth quack prediction, crime analysis, infectious disease diffusion forecasting, condition-based preventative maintenance, information retrieval and behavior-based network analysis and services, etc. Temporal point process (TPP) is a principled mathematical tool for the modeling and learning of asynchronous event sequences, which captures the instantaneous happening rate of the events and the temporal dependency between historical and current events. TPP provides us with an interpretable model to describe the generative mechanism of event sequences, which is beneficial for event prediction and causality analysis. Recently, it has been shown that TPP has potentials to many machine learning and data science applications and can be combined with other cutting-edge machine learning techniques like deep learning, reinforcement learning, adversarial learning, and so on.

In the first part of the tutorial, we will start with an elementary introduction of TPP model, including the basic concepts of the model, the simulation method of event sequences; in the second part of the tutorial, we will introduce typical TPP models and their traditional learning methods; in the third part of the tutorial, we will discuss the recent progress on the modeling and learning of TPP, including neural network-based TPP models, generative adversarial networks (GANs) for TPP, and deep reinforcement learning of TPP; in the final part, we will talk about the practical application of TPP, including useful data augmentation methods for learning from imperfect observations, typical applications and examples like healthcare and industry maintenance, and existing open source toolboxes.

  • Junchi Yan,, Shanghai Jiao Tong University, China
  • Hongteng Xu,, Duke University & Infinia ML, Inc., USA
  • Liangda Li,, Yahoo Research, USA
  • Outline
    • Part 1: Basics and typical models for TPP
      • Event sequence in real world
      • Conditional intensity functions of TPPs
      • Classic learning strategies
      • Simulation and prediction
      • Hawkes processes
      • Open source packages
    • Part 2: Deep networks for temporal point processes
      • Brief on classic statistical learning of TPP
      • Deep learning for TPP
      • Adversarial learning for TPP
      • Reinforcement learning for TPP
      • Embedding for multi-dimensional TPP
    • Part 3: Temporal point processes in practice
      • Typical real-world applications via TPP
      • Dyadic Event in temporal point process
      • Marked Event in temporal point process
      • Cross-domain Event in temporal point process
      • Parametric influence in temporal point process
    Slides and Lecture Notes




    Junchi Yan is a tenure-track independent research professor with Shanghai Jiao Tong University (SJTU), at both Department of Computer Science and Engineering and SJTU AI Institute. Before joining SJTU in April 2018, Junchi has been with IBM Research working on machine learning and computer vision research and applications since April 2011. During that time. he was once a Senior Research Staff Member and Chief Scientist for industrial inspection with IBM China Research Lab, and extensively applied temporal point pro- cess model for industrial preventative maintenance projects. He was also once a visiting researcher with IBM T.J. Watson Research Center (Yorktown Heights), Japan's National Institute of Informatics (Tokyo), and Tecent AI Lab (Shenzhen).

    Hongteng Xu is a research scientist with Infinia ML, Inc. At the same time, he is a visiting faculty with Duke University, at Department of Electrical and Computer Engineering. Before joining Infinia ML in January 2018, Hongteng has been a postdoctoral researcher with Duke University since August 2017. Hongteng holds a Ph.D. in Electrical and Computer Engineering from Georgia Institute of Technology, a dual M.S. degree in Electrical and Computer Engineering from both Georgia Institute of Technology and Shanghai Jiao Tong University. He has been awarded the 2013 Shanghai Outstanding M.S. Thesis Award and the Coulter Fellowship of Gerogia Institute of Technology during 2010-2013.

    Liangda Li is a senior research scientist at Yahoo Research, in the search and search ads team. He lead the vertical search ranking, query understanding, search ads, query linguistic analysis projects of Science team. Before joining Yahoo Research, he earned his Ph.D. degree in the School of Computer Science at Georgia Institute of Technology, under the supervision of Professor Hongyuan Zha. He received his B.S. degree in ACM honored class, at the School of Computer Science from Shanghai Jiao-Tong University in 2010. He has been awarded the 2010 Microsoft Research Asia Young Research Fellow Award. His research interest includes machine learning and its applications in information retrieval and social network. In particular, his focus in on influence modeling in various real-world behavioral data, such as search intent understanding, urban intelligence and crisis/crime.