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We propose a novel adversarial learning strategy for mixture models of Hawkesprocesses, leveraging data augmentation techniques of Hawkes process in theframework of self-paced learning. Instead of learning a mixture model directlyfrom a set of event sequences drawn from different Hawkes processes, theproposed method learns the target model iteratively, which generates "easy"sequences and uses them in an adversarial and self-paced manner. In eachiteration, we first generate a set of augmented sequences from originalobserved sequences. Based on the fact that an easy sample of the target modelcan be an adversarial sample of a misspecified model, we apply a maximumlikelihood estimation with an adversarial self-paced mechanism. In this mannerthe target model is updated, and the augmented sequences that obey it areemployed for the next learning iteration. Experimental results show that theproposed method outperforms traditional methods consistently.

Adversarial Self-Paced Learning for Mixture Models of Hawkes Processes