# Adversarial Self-Paced Learning for Mixture Models of Hawkes Processes

## TL;DR

This paper introduces an adversarial self-paced learning approach for mixture models of Hawkes processes, which iteratively generates and uses augmented sequences to improve model accuracy and robustness.

## Contribution

It presents a novel adversarial self-paced learning framework that leverages data augmentation for mixture models of Hawkes processes, enhancing learning from complex event sequences.

## Key findings

- Outperforms traditional methods in experiments
- Effectively generates augmented sequences for learning
- Improves model robustness and accuracy

## Abstract

We propose a novel adversarial learning strategy for mixture models of Hawkes processes, leveraging data augmentation techniques of Hawkes process in the framework of self-paced learning. Instead of learning a mixture model directly from a set of event sequences drawn from different Hawkes processes, the proposed method learns the target model iteratively, which generates "easy" sequences and uses them in an adversarial and self-paced manner. In each iteration, we first generate a set of augmented sequences from original observed sequences. Based on the fact that an easy sample of the target model can be an adversarial sample of a misspecified model, we apply a maximum likelihood estimation with an adversarial self-paced mechanism. In this manner the target model is updated, and the augmented sequences that obey it are employed for the next learning iteration. Experimental results show that the proposed method outperforms traditional methods consistently.