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Gaussian processes (GPs) are Bayesian non-parametric models useful in amyriad of applications. Despite their popularity, the cost of GP predictions(quadratic storage and cubic complexity with respect to the number of trainingpoints) remains a hurdle in applying GPs to large data. We present a fastposterior mean prediction algorithm called FastMuyGPs to address thisshortcoming. FastMuyGPs is based upon the MuyGPs hyperparameter estimationalgorithm and utilizes a combination of leave-one-out cross-validation,batching, nearest neighbors sparsification, and precomputation to providescalable, fast GP prediction. We demonstrate several benchmarks whereinFastMuyGPs prediction attains superior accuracy and competitive or superiorruntime to both deep neural networks and state-of-the-art scalable GPalgorithms.

Fast Gaussian Process Posterior Mean Prediction via Local Cross Validation and Precomputation