GP

From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior Approximation
With the strengths of both deep learning and kernel methods like Gaussian Processes (GPs), Deep Kernel Learning (DKL) has gained …
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From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior Approximation
Aggregation Models with Optimal Weights for Distributed Gaussian Processes
GP models have received increasingly attentions in recent years due to their super prediction accuracy and modeling flexibility. To …
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Aggregation Models with Optimal Weights for Distributed Gaussian Processes
Gaussian Processes Sampling with Sparse Grids under Additive Schwarz Preconditioner
Gaussian processes (GPs) are widely used in non-parametric Bayesian modeling, and play an important role in various statistical and …
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Gaussian Processes Sampling with Sparse Grids under Additive Schwarz Preconditioner
Improving the Predictability of the Madden-Julian Oscillation at Subseasonal Scales with Gaussian Process Models
The Madden–Julian Oscillation (MJO) is an influential climate phenomenon that plays a vital role in modulating global weather …
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Improving the Predictability of the Madden-Julian Oscillation at Subseasonal Scales with Gaussian Process Models
Kernel Packet: An Exact and Scalable Algorithm for Gaussian Process Regression with Matérn Correlations
We develop an exact and scalable algorithm for one-dimensional Gaussian process regression with Matérn correlations whose smoothness …
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Kernel Packet: An Exact and Scalable Algorithm for Gaussian Process Regression with Matérn Correlations