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cs.LG2020★ 11 cited
A Tutorial on Sparse Gaussian Processes and Variational Inference
Felix Leibfried, Vincent Dutordoir, ST John +1
Gaussian processes (GPs) provide a framework for Bayesian inference that can offer principled uncertainty estimates for a large range of problems. For example, if we consider regre…
cs.LG2018
Scalable GAM using sparse variational Gaussian processes
Vincent Adam, Nicolas Durrande, ST John
Generalized additive models (GAMs) are a widely used class of models of interest to statisticians as they provide a flexible way to design interpretable models of data beyond linea…