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Found 3 papers, 1 papers with code
Scalable Gaussian Process Hyperparameter Optimization via Coverage Regularization
Gaussian processes (GPs) are Bayesian non-parametric models popular in a variety of applications due to their accuracy and native uncertainty quantification (UQ).
Fast Gaussian Process Posterior Mean Prediction via Local Cross Validation and Precomputation
Gaussian processes (GPs) are Bayesian non-parametric models useful in a myriad of applications.
MuyGPs: Scalable Gaussian Process Hyperparameter Estimation Using Local Cross-Validation
Gaussian processes (GPs) are non-linear probabilistic models popular in many applications.
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