An iterative method combining adversarial learning, self-paced learning, and data augmentation to train mixture models of Hawkes processes, with abstract claiming consistent outperformance over traditional methods.
Multi-task multi-dimensional hawkes processes for modeling event sequences
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Adversarial Self-Paced Learning for Mixture Models of Hawkes Processes
An iterative method combining adversarial learning, self-paced learning, and data augmentation to train mixture models of Hawkes processes, with abstract claiming consistent outperformance over traditional methods.