Graduate School and Research Center in Digital Sciences

Random feature expansions for deep Gaussian processes

Cutajar, Kurt; Bonilla, Edwin; Michiardi, Pietro; Filippone, Maurizio

ICML 2017, 34th International Conference on Machine Learning, 6-11 August 2017, Sydney, Australia

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.

Document Bibtex

Title:Random feature expansions for deep Gaussian processes
Department:Data Science
Eurecom ref:5214
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Bibtex: @inproceedings{EURECOM+5214, year = {2017}, title = {{R}andom feature expansions for deep {G}aussian processes}, author = {{C}utajar, {K}urt and {B}onilla, {E}dwin and {M}ichiardi, {P}ietro and {F}ilippone, {M}aurizio }, booktitle = {{ICML} 2017, 34th {I}nternational {C}onference on {M}achine {L}earning, 6-11 {A}ugust 2017, {S}ydney, {A}ustralia}, address = {{S}ydney, {AUSTRALIA}}, month = {08}, url = {} }
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