GPLFR jointly learns compression and GP regression for high-dimensional outputs, outperforming PCA-GP under structured noise and enabling a spatially resolved rocky-exoplanet climate emulator.
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Gaussian Process Latent Factor Regression for Low-Data, High-Dimensional Output Problems
GPLFR jointly learns compression and GP regression for high-dimensional outputs, outperforming PCA-GP under structured noise and enabling a spatially resolved rocky-exoplanet climate emulator.
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