Distributed robust Gaussian Process regression

Publikation: Beiträge in ZeitschriftenZeitschriftenaufsätzeForschungbegutachtet

Authors

We study distributed and robust Gaussian Processes where robustness is introduced by a Gaussian Process prior on the function values combined with a Student-t likelihood. The posterior distribution is approximated by a Laplace Approximation, and together with concepts from Bayesian Committee Machines, we efficiently distribute the computations and render robust GPs on huge data sets feasible. We provide a detailed derivation and report on empirical results. Our findings on real and artificial data show that our approach outperforms existing baselines in the presence of outliers by using all available data.
OriginalspracheEnglisch
ZeitschriftKnowledge and Information Systems
Jahrgang55
Ausgabenummer2
Seiten (von - bis)415-435
Anzahl der Seiten21
ISSN0219-1377
DOIs
PublikationsstatusErschienen - 01.05.2018

DOI