CWGP builds non-Gaussian GPs from compositions of elementary warpings with explicit inverses, delivering analytical predictions and improved accuracy and speed over standard WGPs.
Bengio, et al., Learning deep architectures for AI, Foundations and trendsR⃝ in Machine Learning 2 (1) (2009) 1–127
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Compositionally-Warped Gaussian Processes
CWGP builds non-Gaussian GPs from compositions of elementary warpings with explicit inverses, delivering analytical predictions and improved accuracy and speed over standard WGPs.