2024
Robust Gaussian Processes via Relevance Pursuit
Sebastian Ament, Elizabeth Santorella, David Eriksson, Benjamin Letham, Maximilian Balandat, Eytan Bakshy
NeurIPS 2024poster
Gaussian processes (GPs) are non-parametric probabilistic regression models that are popular due to their flexibility, data efficiency, and well-calibrated uncertainty estimates. However, standard GP models assume homoskedastic Gaussian noise, while many real-world applications are subject to non-Ga…