AMultitask Point Process Predictive Model

Point process data are commonly observed in fields like healthcare and the social sciences. De-signing predictive models for such event streams is an under-explored problem, due to often scarce training data. In this work we propose a multi-task point process model, leveraging information from all tasks via a hierarchical Gaussian pro-cess (GP). Nonparametric learning functions im-plemented by a GP, which map from past events to future rates, allow analysis of flexible arrival patterns. To facilitate efficient inference, we pro-pose a sparse construction for this hierarchical model, and derive a variational Bayes method for learning and inference. Experimental results are shown on both synthetic data and as well as real electronic health-records data. 1