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- Title
Distributed robust Gaussian Process regression.
- Authors
Mair, Sebastian; Brefeld, Ulf
- Abstract
We study distributed and robust Gaussian Processes where robustness is introduced by a Gaussian Process prior on the function values combined with a Student-<italic>t</italic> 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.
- Subjects
GAUSSIAN processes; ROBUST statistics; BAYESIAN analysis; BIG data; OUTLIERS (Statistics); DISTRIBUTED computing; REGRESSION analysis
- Publication
Knowledge & Information Systems, 2018, Vol 55, Issue 2, p415
- ISSN
0219-1377
- Publication type
Article
- DOI
10.1007/s10115-017-1084-7