it's more than a variance reduction method...
Zhang, Yunhao, and Junchi Yan. 2021. “Neural Relation Inference for Multi-Dimensional Temporal Point Processes via Message Passing Graph.” In , 3:3406–12. https://doi.org/10.24963/ijcai.2021/469.
A multidimentional(D nodes) marked(M types of possible events) TPP($N_m$ length of sequence) modeling
Shchur, Oleksandr, Ali Caner Türkmen, Tim Januschowski, and Stephan Günnemann. 2021. “Neural Temporal Point Processes: A Review.” ArXiv:2104.03528 [Cs], August. http://arxiv.org/abs/2104.03528.
Neural TPP = TPP + Deep Learning
Model Sequence Events: any sequential model(RNN, transfomer) + TPP
The timing modeling is not embedded in the complex structure of RNN, so the pdf of $t$ could be solved analytically
Modeling of timing is embedded into the LSTM structure. so the hidden state $h(t)$ is a function of $t$(continuous)
Hawkes Process is a special kind of point process with 1. postive 2. additive 3. exponentail decay self-exiciting factor 2.
Point Process is widely used in modeling sequence of events that: 1. timing is important 2.rare to happen