Senior ResearcherVicarious AI
Predictive Models for Point Processes
Point process data are commonly observed in fields like healthcare and social science. Designing predictive models for such event streams is an under-explored problem, due to often scarce training data. In this thesis, a multitask point process model via a hierarchical Gaussian Process (GP) is proposed, to leverage statistical strength across multiple point processes. Nonparametric learning functions implemented by a GP, which map from past events to future rates, allow analysis of flexible arrival patterns. To facilitate efficient inference, a sparse construction for this hierarchical model is proposed, and a variational Bayes method is derived for learning and inference. Experimental results are shown on both synthetic data and as well as real electronic health-records data.