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Sequential Design for Simulators with Sharp Variations via Deep Gaussian Process Gradients
Deep Gaussian Processes (DGPs) compose Gaussian Process (GP) layers to warp inputs, improving the emulation of computer simulators with nonstationary input-output mappings. While gradient posteriors are analytically available for GPs, quantifying gradient uncertainty for DGPs is more challenging due
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