Reviews: Meta Learning with Relational Information for Short Sequences

In this paper, the authors propose a hierarchical Bayesian mixture of Hawkes processes with a parameter adaptation mechanism based on a meta-learning technique for modeling multiple short event sequences with graph-like side information. In the proposed model, each sequence is modeled by a mixture of Hawkes processes, whose mixture ratio has relation to the adjacency of the sequence to the other sequences. Moreover, the parameters of the component Hawkes processes are slightly varied among sequences using the mechanism of the model-agnostic meta-learning framework. The authors provide experimental results on synthetic and real-world datasets, which show the superiority of the proposed method. Overall, the paper is very well written.