NeurIPS 2016poster340 citations
Understanding Probabilistic Sparse Gaussian Process Approximations
Matthias Bauer, Mark van der Wilk, Carl Edward Rasmussen
Abstract
Good sparse approximations are essential for practical inference in Gaussian Processes as the computational cost of exact methods is prohibitive for large datasets. The Fully Independent Training Conditional (FITC) and the Variational Free Energy (VFE) approximations are two recent popular methods. Despite superficial similarities, these approximations have surprisingly different theoretical properties and behave differently in practice. We thoroughly investigate the two methods for regression both analytically and through illustrative examples, and draw conclusions to guide practical application.
BibTeX
@inproceedings{NIPS2016_7250eb93,
author = {Bauer, Matthias and van der Wilk, Mark and Rasmussen, Carl Edward},
booktitle = {Advances in Neural Information Processing Systems},
editor = {D. Lee and M. Sugiyama and U. Luxburg and I. Guyon and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Understanding Probabilistic Sparse Gaussian Process Approximations},
url = {https://proceedings.neurips.cc/paper_files/paper/2016/file/7250eb93b3c18cc9daa29cf58af7a004-Paper.pdf},
volume = {29},
year = {2016}
}