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

This paper proposes Gaussian Process Latent Factor Regression (GPLFR), a model designed for predicting high-dimensional outputs from few training examples. It couples compression and prediction in a single objective to handle high dimensionality. GPLFR is demonstrated by building the first spatially resolved emulator of global climate models for rocky exoplanets.