4 citations · 9 across the 7 of their papers we have counts for
7 papers
Deep graph kernel point processes over networks
Zheng Dong, Matthew Repasky, Xiuyuan Cheng +1
Point process models are widely used for continuous-time discrete-event data, where each data point includes time and additional information called "marks," such as locations, node…
Conditional Generative Modeling for High-dimensional Marked Temporal Point Processes
Zheng Dong, Zekai Fan, Shixiang Zhu
Point processes offer a versatile framework for sequential event modeling. However, the computational challenges and constrained representational power of the existing point proces…
Spatio-temporal point processes with deep non-stationary kernels
Zheng Dong, Xiuyuan Cheng, Yao Xie
Point process data are becoming ubiquitous in modern applications, such as social networks, health care, and finance. Despite the powerful expressiveness of the popular recurrent n…
Tides Need STEMMED: A Locally Operating Spatio-Temporal Mutually Exciting Point Process with Dynamic Network for Improving Opioid Overdose Death Prediction
Che-Yi Liao, Gian-Gabriel P. Garcia, Kamran Paynabar +3
We develop a Spatio-TEMporal Mutually Exciting point process with Dynamic network (STEMMED), i.e., a point process network wherein each node models a unique community-drug event st…
Non-stationary spatio-temporal point process modeling for high-resolution COVID-19 data
Zheng Dong, Shixiang Zhu, Yao Xie +2
Most COVID-19 studies commonly report figures of the overall infection at a state- or county-level. This aggregation tends to miss out on fine details of virus propagation. In this…
Multi-resolution spatio-temporal prediction with application to wind power generation
Zheng Dong, Hanyu Zhang, Shixiang Zhu +2
Wind energy is becoming an increasingly crucial component of a sustainable grid, but its inherent variability and limited predictability present challenges for grid operators. The…
Neural Spectral Marked Point Processes
Shixiang Zhu, Haoyun Wang, Zheng Dong +2
Self- and mutually-exciting point processes are popular models in machine learning and statistics for dependent discrete event data. To date, most existing models assume stationary…