Reviews: Meta Learning with Relational Information for Short Sequences

The paper proposes a hierarchical model for multivariate point process data with known network information. It uses a mixture of Hawkes processes for the point process observations, and the treats the observed network as a mixed membership stochastic block model sharing the same mixture weights. The main technical novelty is to use model agnostic meta-learning (MAML) to implement the hierarchical prior on the Hawkes process parameters. However, this technical contribution (MAML Hawkes/network models) is not compared to standard hierarchical Bayesian techniques. Specifically, the parameters \theta_{k} {(i)} are only three dimensional (background rate \mu, scale \delta, and time constant \omega).