Automatic Integration for Fast and Interpretable Neural Point Processes

Zihao Zhou, Rose Yu
Proceedings of The 5th Annual Learning for Dynamics and Control Conference, PMLR 211:573-585, 2023.

Abstract

The fundamental bottleneck of learning continuous-time point processes is integration. Due to the intrinsic mathematical difficulty of symbolic integration, neural point process (NPP) models either constrain the intensity function to a simple integrable kernel function or apply numerical integration. However, the former has limited expressive power. The latter suffers additional numerical errors and high computational costs. In this paper, we introduce *Automatic Integration for Neural Point Process* models (Auto-NPP), a new paradigm for exact, efficient, non-parametric inference of point process. We validate our method on simulated events governed by temporal point processes and real-world events. We demonstrate that our method has clear advantages in recovering complex intensity functions from irregular time series. On real-world datasets with noise and unknown intensity functions, our method is also much faster than state-of-the-art NPP models with comparable prediction accuracy.

Cite this Paper

BibTeX
@InProceedings{pmlr-v211-zhou23a, title = {Automatic Integration for Fast and Interpretable Neural Point Processes}, author = {Zhou, Zihao and Yu, Rose}, booktitle = {Proceedings of The 5th Annual Learning for Dynamics and Control Conference}, pages = {573--585}, year = {2023}, editor = {Matni, Nikolai and Morari, Manfred and Pappas, George J.}, volume = {211}, series = {Proceedings of Machine Learning Research}, month = {15--16 Jun}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v211/zhou23a/zhou23a.pdf}, url = {https://proceedings.mlr.press/v211/zhou23a.html}, abstract = {The fundamental bottleneck of learning continuous-time point processes is integration. Due to the intrinsic mathematical difficulty of symbolic integration, neural point process (NPP) models either constrain the intensity function to a simple integrable kernel function or apply numerical integration. However, the former has limited expressive power. The latter suffers additional numerical errors and high computational costs. In this paper, we introduce *Automatic Integration for Neural Point Process* models (Auto-NPP), a new paradigm for exact, efficient, non-parametric inference of point process. We validate our method on simulated events governed by temporal point processes and real-world events. We demonstrate that our method has clear advantages in recovering complex intensity functions from irregular time series. On real-world datasets with noise and unknown intensity functions, our method is also much faster than state-of-the-art NPP models with comparable prediction accuracy. } }
Endnote
%0 Conference Paper %T Automatic Integration for Fast and Interpretable Neural Point Processes %A Zihao Zhou %A Rose Yu %B Proceedings of The 5th Annual Learning for Dynamics and Control Conference %C Proceedings of Machine Learning Research %D 2023 %E Nikolai Matni %E Manfred Morari %E George J. Pappas %F pmlr-v211-zhou23a %I PMLR %P 573--585 %U https://proceedings.mlr.press/v211/zhou23a.html %V 211 %X The fundamental bottleneck of learning continuous-time point processes is integration. Due to the intrinsic mathematical difficulty of symbolic integration, neural point process (NPP) models either constrain the intensity function to a simple integrable kernel function or apply numerical integration. However, the former has limited expressive power. The latter suffers additional numerical errors and high computational costs. In this paper, we introduce *Automatic Integration for Neural Point Process* models (Auto-NPP), a new paradigm for exact, efficient, non-parametric inference of point process. We validate our method on simulated events governed by temporal point processes and real-world events. We demonstrate that our method has clear advantages in recovering complex intensity functions from irregular time series. On real-world datasets with noise and unknown intensity functions, our method is also much faster than state-of-the-art NPP models with comparable prediction accuracy.
APA
Zhou, Z. & Yu, R.. (2023). Automatic Integration for Fast and Interpretable Neural Point Processes. Proceedings of The 5th Annual Learning for Dynamics and Control Conference, in Proceedings of Machine Learning Research 211:573-585 Available from https://proceedings.mlr.press/v211/zhou23a.html.

Related Material