Gaussian Process Latent Factor Regression for Low-Data, High-Dimensional Output Problems

In three linesNew GPLFR method combining Gaussian processes with linear-Gaussian decoding to predict high-dimensional outputs from few training examples. Analytical marginalization of decoder weights couples compression and prediction in a single objective. Application: first spatially resolved emulator of global climate models for rocky exoplanets.

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