Tag: kernels
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A reproducible comparative study of categorical kernels for Gaussian process regression, with new clustering-based nested kernels
A reproducible comparative study of categorical kernels for Gaussian process regression, with new clustering-based nested kernels arXiv:2510.01840v1 Announce Type: new Abstract: Designing categorical kernels is a major challenge for Gaussian process regression with continuous and categorical inputs. Despite previous studies, it is difficult to identify a preferred method, either because the evaluation metrics, the optimization…
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Interpretable Kernels
Interpretable Kernels arXiv:2508.15932v1 Announce Type: new Abstract: The use of kernels for nonlinear prediction is widespread in machine learning. They have been popularized in support vector machines and used in kernel ridge regression, amongst others. Kernel methods share three aspects. First, instead of the original matrix of predictor variables or features, each observation is mapped…
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Unified Native Spaces in Kernel Methods
Unified Native Spaces in Kernel Methods arXiv:2501.01825v1 Announce Type: new Abstract: There exists a plethora of parametric models for positive definite kernels, and their use is ubiquitous in disciplines as diverse as statistics, machine learning, numerical analysis, and approximation theory. Usually, the kernel parameters index certain features of an associated process. Amongst those features, smoothness…