Spectral Mixture Kernels for Multi-Output Gaussian Processes

NeurIPS 2017 Gabriel ParraFelipe Tobar

Early approaches to multiple-output Gaussian processes (MOGPs) relied on linear combinations of independent, latent, single-output Gaussian processes (GPs). This resulted in cross-covariance functions with limited parametric interpretation, thus conflicting with the ability of single-output GPs to understand lengthscales, frequencies and magnitudes to name a few... (read more)

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