Gaussian Cox Process

Gaussian Cox processes (GCPs) model the intensity of point events in space and/or time as a latent Gaussian process, offering a flexible framework for analyzing clustered or spatially varying event data. Current research focuses on developing efficient inference methods, particularly for complex scenarios like multi-task learning (combining different data types) and spatiotemporal modeling using extensions such as Hawkes processes. These advancements enable improved analysis of diverse datasets, including those from epidemiology, criminology, and environmental monitoring, by providing more accurate and computationally tractable models for complex event patterns. The resulting insights can lead to better predictions and a deeper understanding of underlying processes generating the observed events.

Papers

October 8, 2024

Federated Neural Nonparametric Point Processes
Hui Chen, Hengyu Liu, Yaqiong Li, Xuhui Fan, Zhilin Zhao, Feng Zhou, Christopher John Quinn, Longbing Cao
Temporal Point Process Client Model Neural Point Process Gaussian Cox Process

January 25, 2024

Bayesian Optimization through Gaussian Cox Process Models for Spatio-temporal Data
Yongsheng Mei, Mahdi Imani, Tian Lan
Gaussian Process Bayesian Optimization Temporal Data Posteriori Error Gaussian Cox Process

August 29, 2023

Heterogeneous Multi-Task Gaussian Cox Processes
Feng Zhou, Quyu Kong, Zhijie Deng, Fengxiang He, Peng Cui, Jun Zhu
Gaussian Process Multi Output Gaussian Process Heterogeneous Task Gaussian Cox Process

October 21, 2022

Cox-Hawkes: doubly stochastic spatiotemporal Poisson processes
Xenia Miscouridou, Samir Bhatt, George Mohler, Seth Flaxman, Swapnil Mishra
Hawkes Process Point Process Spatiotemporal Dynamic Gaussian Cox Process

Gaussian Cox Process

Papers

Federated Neural Nonparametric Point Processes

Bayesian Optimization through Gaussian Cox Process Models for Spatio-temporal Data

Heterogeneous Multi-Task Gaussian Cox Processes

Cox-Hawkes: doubly stochastic spatiotemporal Poisson processes