NeurIPS 2019poster3 citations
Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees
Muhammad Osama, Dave Zachariah, Peter Stoica
Abstract
A spatial point process can be characterized by an intensity function which predicts the number of events that occur across space. In this paper, we develop a method to infer predictive intensity intervals by learning a spatial model using a regularized criterion. We prove that the proposed method exhibits out-of-sample prediction performance guarantees which, unlike standard estimators, are valid even when the spatial model is misspecified. The method is demonstrated using synthetic as well as real spatial data.
BibTeX
@inproceedings{NEURIPS2019_ee0c1616,
author = {Osama, Muhammad and Zachariah, Dave and Stoica, Peter},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees},
url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/ee0c1616bbc82804b2f4b635d4a055fb-Paper.pdf},
volume = {32},
year = {2019}
}