  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.
