# Fast Learning of Multidimensional Hawkes Processes via Frank-Wolfe

arXiv: https://arxiv.org/abs/2212.06081 · HTML: https://mlsanity.com/paper/2212.06081/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 12 Dec 2022.

## Builds on (most cited first, 10 of 10)

- [Transformer Hawkes Process](https://mlsanity.com/paper/2002.09291/) (2020)
- [Relatively-Smooth Convex Optimization by First-Order Methods, and Applications](https://mlsanity.com/paper/1610.05708/) (2016)
- [Learning Granger Causality for Hawkes Processes](https://mlsanity.com/paper/1602.04511/) (2016)
- [Self-Attentive Hawkes Processes](https://mlsanity.com/paper/1907.07561/) (2019)
- [Shaping Social Activity by Incentivizing Users](https://mlsanity.com/paper/1408.0406/) (2014)
- [Analysis of the Frank-Wolfe Method for Convex Composite Optimization involving a Logarithmically-Homogeneous Barrier](https://mlsanity.com/paper/2010.08999/) (2020)
- [Inexact Relative Smoothness and Strong Convexity for Optimization and Variational Inequalities by Inexact Model](https://mlsanity.com/paper/2001.09013/) (2020)
- [Dual optimization for convex constrained objectives without the gradient-Lipschitz assumption](https://mlsanity.com/paper/1807.03545/) (2018)
- [Online Learning for Mixture of Multivariate Hawkes Processes](https://mlsanity.com/paper/2208.07961/) (2022)
- [Differentially Private Learning of Hawkes Processes](https://mlsanity.com/paper/2207.13741/) (2022)

## Abstract (arXiv)

Hawkes processes have recently risen to the forefront of tools when it comes to modeling and generating sequential events data. Multidimensional Hawkes processes model both the self and cross-excitation between different types of events and have been applied successfully in various domain such as finance, epidemiology and personalized recommendations, among others. In this work we present an adaptation of the Frank-Wolfe algorithm for learning multidimensional Hawkes processes. Experimental results show that our approach has better or on par accuracy in terms of parameter estimation than other first order methods, while enjoying a significantly faster runtime.