Abstract: Gaussian Process regression is a powerful non-parametric approach that facilitates probabilistic uncertainty quantification in machine learning. Distributed Gaussian Process (DGP) methods ...
Abstract: Inducing-point-based sparse variational approximation scales Gaussian process models to large datasets but tends to overestimate observation noise and underestimate posterior variance.
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