Name
Abstract | ||
|---|---|---|
Point process data are commonly observed in fields like healthcare and the social sciences. Designing predictive models for such event streams is an under-explored problem, due to often scarce training data. In this work we propose a multitask point process model, leveraging information from all tasks via a hierarchical Gaussian process (GP). 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, we propose 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. |
Year | Venue | Field |
|---|---|---|
2015 | International Conference on Machine Learning | Training set,Data mining,Computer science,Inference,Point process,Nonparametric statistics,Synthetic data,Gaussian process,Artificial intelligence,Hierarchical database model,Machine learning,Bayes' theorem |
DocType | Citations | PageRank |
Conference | 13 | 0.70 |
References | Authors | |
13 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenzhao Lian | 1 | 39 | 3.68 |
| Ricardo Henao | 2 | 286 | 23.85 |
| Vinayak Rao | 3 | 127 | 12.62 |
| Joseph Lucas | 4 | 17 | 1.80 |
| Lawrence Carin | 5 | 137 | 11.38 |