SAC 2019, 34th ACM/SIGAPP Symposium on Applied Computing, April 8-12, 2019, Limassol, Cyprus
Gaussian Processes (GPs) are powerful non-parametric Bayesian models for function estimation, but suffer from high complexity in terms of both computation and storage. To address such issues, approximation methods have flourished in the literature, including model approximations and approximate inference. However, these methods often sacrifice accuracy for scalability. In this work, we present the design and evaluation of a distributed method for exact GP inference, that achieves true model parallelism
using simple, high-level distributed computing frameworks. Our experiments show that exact inference at scale is not only feasible, but it also brings substantial benefits in terms of low error rates and accurate quantification of uncertainty.
© ACM, 2019. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in SAC 2019, 34th ACM/SIGAPP Symposium on Applied Computing, April 8-12, 2019, Limassol, Cyprus http://dx.doi.org/10.1145/3297280.3297409