A multitask point process predictive model

Published

Conference Paper

© Copyright 2015 by International Machine Learning Society (IMLS). All rights reserved. Point process data are commonly observed in fields like healthcare and the social sciences. Designing predictive models for such event streams is an under-explored problem, due to often scarce training data. In this work we propose a multitask point process model, leveraging information from all tasks via a hierarchical Gaussian process (GP). Nonparametric learning functions implemented by a GP, which map from past events to future rates, allow analysis of flexible arrival patterns. To facilitate efficient inference, we propose a sparse construction for this hierarchical model, and derive a variational Bayes method for learning and inference. Experimental results are shown on both synthetic data and as well as real electronic health-records data.

Duke Authors

Cited Authors

  • Lian, W; Henao, R; Rao, V; Lucas, J; Carin, L

Published Date

  • January 1, 2015

Published In

  • 32nd International Conference on Machine Learning, Icml 2015

Volume / Issue

  • 3 /

Start / End Page

  • 2030 - 2038

International Standard Book Number 13 (ISBN-13)

  • 9781510810587

Citation Source

  • Scopus