2017-07-17-cutajar17a.md

title	booktitle	year	volume	series	address	month	publisher	pdf	url	abstract	layout	id	tex_title	bibtex_author	firstpage	lastpage	page	order	cycles	editor	author	date	container-title	genre	issued	extras
Random Feature Expansions for Deep Gaussian Processes	Proceedings of the 34th International Conference on Machine Learning	2017	70	Proceedings of Machine Learning Research		0	PMLR	http://proceedings.mlr.press/v70/cutajar17a/cutajar17a.pdf	http://proceedings.mlr.press/v70/cutajar17a.html	The composition of multiple Gaussian Processes as a Deep Gaussian Process DGP enables a deep probabilistic nonparametric approach to flexibly tackle complex machine learning problems with sound quantification of uncertainty. Existing inference approaches for DGP models have limited scalability and are notoriously cumbersome to construct. In this work we introduce a novel formulation of DGPs based on random feature expansions that we train using stochastic variational inference. This yields a practical learning framework which significantly advances the state-of-the-art in inference for DGPs, and enables accurate quantification of uncertainty. We extensively showcase the scalability and performance of our proposal on several datasets with up to 8 million observations, and various DGP architectures with up to 30 hidden layers.	inproceedings	cutajar17a	Random Feature Expansions for Deep {G}aussian Processes	Kurt Cutajar and Edwin V. Bonilla and Pietro Michiardi and Maurizio Filippone	884	893	884-893	884	false	given family Doina Precup given family Yee Whye Teh	given family Kurt Cutajar given family Edwin V. Bonilla given family Pietro Michiardi given family Maurizio Filippone	2017-07-17	Proceedings of the 34th International Conference on Machine Learning	inproceedings	date-parts 2017 7 17	label link Supplementary PDF http://proceedings.mlr.press/v70/cutajar17a/cutajar17a-supp.pdf