4 citations · 4 across the 5 of their papers we have counts for
5 papers
TransFeat-TPP: An Interpretable Deep Covariate Temporal Point Processes
Zizhuo Meng, Boyu Li, Xuhui Fan +4
The classical temporal point process (TPP) constructs an intensity function by taking the occurrence times into account. Nevertheless, occurrence time may not be the only relevant…
Interpretable Transformer Hawkes Processes: Unveiling Complex Interactions in Social Networks
Zizhuo Meng, Ke Wan, Yadong Huang +3
Social networks represent complex ecosystems where the interactions between users or groups play a pivotal role in information dissemination, opinion formation, and social interact…
Additive Poisson Process: Learning Intensity of Higher-Order Interaction in Stochastic Processes
Simon Luo, Feng Zhou, Lamiae Azizi +1
We present the Additive Poisson Process (APP), a novel framework that can model the higher-order interaction effects of the intensity functions in stochastic processes using lower…
Fast Multi-resolution Segmentation for Nonstationary Hawkes Process Using Cumulants
Feng Zhou, Zhidong Li, Xuhui Fan +3
The stationarity is assumed in vanilla Hawkes process, which reduces the model complexity but introduces a strong assumption. In this paper, we propose a fast multi-resolution segm…
Efficient EM-Variational Inference for Hawkes Process
Feng Zhou, Zhidong Li, Xuhui Fan +3
In classical Hawkes process, the baseline intensity and triggering kernel are assumed to be a constant and parametric function respectively, which limits the model flexibility. To…