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Scalable Gaussian Process Regression for Kernels with a Non-Stationary Phase
Our approach employs multiple sets of non-equidistant inducing points to account for the non-stationarity and retrieve Toeplitz and Kronecker structure in the kernel matrix allowing for efficient inference.
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