Name
Abstract | ||
|---|---|---|
Sparse pseudo-point approximations for Gaussian process (GP) models provide a suite of methods that support deployment of GPs in the large data regime and enable analytic intractabilities to be sidestepped. However, the field lacks a principled method to handle streaming data in which both the posterior distribution over function values and the hyperparameter estimates are updated in an online fashion. The small number of existing approaches either use suboptimal hand-crafted heuristics for hyperparameter learning, or suffer from catastrophic forgetting or slow updating when new data arrive. This paper develops a new principled framework for deploying Gaussian process probabilistic models in the streaming setting, providing methods for learning hyperparameters and optimising pseu do-input locations. The proposed framework is assessed using synthetic and real-world datasets. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.17863/CAM.21293 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017) |
Field | DocType | Volume |
Forgetting,Hyperparameter,Computer science,Sparse approximation,Posterior probability,Heuristics,Gaussian process,Artificial intelligence,Global Positioning System,Probabilistic logic,Machine learning | Conference | 30 |
ISSN | Citations | PageRank |
1049-5258 | 2 | 0.37 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bui, Thang D. | 1 | 57 | 5.77 |
| Cuong Nguyen | 2 | 207 | 35.89 |
| Richard E. Turner | 3 | 322 | 37.95 |