Learning with Temporal Point Processes
ICML 2018 Tutorial

Basic details

In recent years, there has been an increasing number of machine learning models, inference methods and control algorithms using temporal point processes (TPPs). They have been particularly popular for understanding, predicting, and enhancing the functioning of social and information systems, where they have achieved unprecedented performance. This tutorial aims to introduce temporal point processes to the machine learning community at large.

In the first part of the tutorial, we will first provide an introduction to the basic theory of temporal point processes, then revisit several types of points processes, and finally introduce advanced concepts such as marks and dynamical systems with jumps. In the second and third parts of the tutorial, we will explain how temporal point processes have been used in developing a variety of recent machine learning models and control algorithms, respectively. Therein, we will revisit recent advances related to, e.g., deep learning, Bayesian nonparametrics, causality, stochastic optimal control and reinforcement learning. In each of the above parts, we will highlight open problems and future research to facilitate further research in temporal point processes within the machine learning community.

Timeline

9:15 — 10:00
Temporal point processes: Introduction
10:00 — 10:55
Models and Inference
10:55 — 11:30
RL and Control

Resources

Summary and Slides
References
Software
  • PtPack (C++ TPP library)
  • Tick (Python TPP library)
  • NL MPI-SWS (Several TPP models/control algorithms)

Presenters