ICML 2016poster7 citations
Extended and Unscented Kitchen Sinks
Edwin Bonilla, Daniel Steinberg, Alistair Reid
Abstract
We propose a scalable multiple-output generalization of unscented and extended Gaussian processes. These algorithms have been designed to handle general likelihood models by linearizing them using a Taylor series or the Unscented Transform in a variational inference framework. We build upon random feature approximations of Gaussian process covariance functions and show that, on small-scale single-task problems, our methods can attain similar performance as the original algorithms while having less computational cost. We also evaluate our methods at a larger scale on MNIST and on a seismic inversion which is inherently a multi-task problem.
BibTeX
@InProceedings{pmlr-v48-bonilla16,
title = {Extended and Unscented Kitchen Sinks},
author = {Bonilla, Edwin and Steinberg, Daniel and Reid, Alistair},
booktitle = {Proceedings of The 33rd International Conference on Machine Learning},
pages = {1651--1659},
year = {2016},
editor = {Balcan, Maria Florina and Weinberger, Kilian Q.},
volume = {48},
series = {Proceedings of Machine Learning Research},
address = {New York, New York, USA},
month = {20--22 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v48/bonilla16.pdf},
url = {https://proceedings.mlr.press/v48/bonilla16.html},
abstract = {We propose a scalable multiple-output generalization of unscented and extended Gaussian processes. These algorithms have been designed to handle general likelihood models by linearizing them using a Taylor series or the Unscented Transform in a variational inference framework. We build upon random feature approximations of Gaussian process covariance functions and show that, on small-scale single-task problems, our methods can attain similar performance as the original algorithms while having less computational cost. We also evaluate our methods at a larger scale on MNIST and on a seismic inversion which is inherently a multi-task problem.}
}