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We propose non-stationary spectral kernels for Gaussian process regression.
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Nonstationary covariance functions for Gaussian process regression
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Assessing approximate inference for binary Gaussian process classification
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Spatial modelling using a new class of nonstationary covariance functions
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Modelling non-stationary gene regulatory processes with a non-homogeneous bayesian network and the allocation sampler
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Scalable Inference for Structured Gaussian Process Models
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Gaussian process kernels for pattern discovery and extrapolation
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Input warping for bayesian optimization of non-stationary functions
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Expectation propagation for nonstationary heteroscedastic Gaussian process regression
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Fast kronecker inference in Gaussian processes with non-Gaussian likelihoods
S. Flaxman, A. G. Wilson, D. Neill, H. Nickisch, and A. Smola · 2015
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