NNGP and GPnn achieve universal consistency, attain Stone's minimax L2-risk rate n^{-2α/(2p+d)}, and exhibit asymptotic robustness to hyperparameter choice under mild regularity assumptions.
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The Theory and Practice of Highly Scalable Gaussian Process Regression with Nearest Neighbours
NNGP and GPnn achieve universal consistency, attain Stone's minimax L2-risk rate n^{-2α/(2p+d)}, and exhibit asymptotic robustness to hyperparameter choice under mild regularity assumptions.