Advanced Stationary and Non-Stationary Kernel Designs for Domain-Aware Gaussian Processes. (arXiv:2102.03432v3 [stat.ML] UPDATED)

Web: http://arxiv.org/abs/2102.03432

Gaussian process regression is a widely-applied method for function
approximation and uncertainty quantification. The technique has gained
popularity recently in the machine learning community due to its robustness and
interpretability. The mathematical methods we discuss in this paper are an
extension of the Gaussian-process framework. We are proposing advanced kernel
designs that only allow for functions with certain desirable characteristics to
be elements of the reproducing kernel Hilbert space (RKHS) that underlies all
kernel methods and serves as the sample …

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