arXiv stat.ML
· Papers
Gaussian Process Latent Factor Regression for Low-Data, High-Dimensional Output Problems
arXiv:2606.06576v2 Announce Type: replace-cross Abstract: In the sciences, regression tasks often require predicting high-dimensional outputs from few training examples. Multi-output Gaussian processes excel in low-data regimes but typically struggle with high-dimensional outputs. Compress-then-predict pipelines such a