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We introduce a new structured kernel interpolation (SKI) framework, which generalises and unifies inducing point methods for scalable Gaussian processes (GPs).
A unifying view of sparse approximate Gaussian process regression
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Fastfood-computing Hilbert space expansions in loglinear time
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Fast near-GRID Gaussian process regression
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Gaussian process kernels for pattern discovery and extrapolation
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How to scale up kernel methods to be as good as deep neural nets
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