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Gaussian Processes for Big Data through Stochastic Variational Inference
by
James Hensman
,
Neil Lawrence
| Citations: | 1 - 1 self |
BibTeX
@MISC{Hensman_gaussianprocesses,
author = {James Hensman and Neil Lawrence},
title = {Gaussian Processes for Big Data through Stochastic Variational Inference},
year = {}
}
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Abstract
Gaussian processes [GP 10] are perhaps the dominant approach for inference on functions. They underpin a range of algorithms for regression, classification and unsupervised learning. Unfortunately, exact inference in a GP has complexity O(n 3) with storage demands of O(n 2) and this hinders application of these models for ‘big data’. Various approximate techniques have been suggested [see
