2 papers
cs.LG2026
Bayesian Scattering: A Principled Baseline for Uncertainty on Image Data
Bernardo Fichera, Zarko Ivkovic, Kjell Jorner +2
Uncertainty quantification for image data is dominated by complex deep learning methods, yet the field lacks an interpretable, mathematically grounded baseline. We propose Bayesian…
cs.LG2024
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference
Jonathan Wenger, Kaiwen Wu, Philipp Hennig +3
Model selection in Gaussian processes scales prohibitively with the size of the training dataset, both in time and memory. While many approximations exist, all incur inevitable app…