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Found 3 papers, 1 papers with code
Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks
For regression tasks, standard Gaussian processes (GPs) provide natural uncertainty quantification, while deep neural networks (DNNs) excel at representation learning.
Variational sparse inverse Cholesky approximation for latent Gaussian processes via double Kullback-Leibler minimization
To achieve scalable and accurate inference for latent Gaussian processes, we propose a variational approximation based on a family of Gaussian distributions whose covariance matrices have sparse inverse Cholesky (SIC) factors.
Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian Processes
We focus on the use of our warped Vecchia GP in trust-region Bayesian optimization via Thompson sampling.
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