# Tensor Kernel Recovery for Spatio-Temporal Hawkes Processes

arXiv: https://arxiv.org/abs/2011.12151 · HTML: https://mlsanity.com/paper/2011.12151/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [Flexible Parametric Inference for Space-Time Hawkes Processes](https://mlsanity.com/paper/2406.06849/) (2024)
- [Differentially Private Estimation of Hawkes Process](https://mlsanity.com/paper/2209.07303/) (2022)

## Abstract (arXiv)

We estimate the general influence functions for spatio-temporal Hawkes processes using a tensor recovery approach by formulating the location dependent influence function that captures the influence of historical events as a tensor kernel. We assume a low-rank structure for the tensor kernel and cast the estimation problem as a convex optimization problem using the Fourier transformed nuclear norm (TNN). We provide theoretical performance guarantees for our approach and present an algorithm to solve the optimization problem. Moreover, we demonstrate the efficiency of our estimation with numerical simulations.