3 papers
cs.LG2024
Non-Asymptotic Uncertainty Quantification in High-Dimensional Learning
Frederik Hoppe, Claudio Mayrink Verdun, Hannah Laus +2
Uncertainty quantification (UQ) is a crucial but challenging task in many high-dimensional regression or learning problems to increase the confidence of a given predictor. We devel…
eess.SP2024
With or Without Replacement? Improving Confidence in Fourier Imaging
Frederik Hoppe, Claudio Mayrink Verdun, Felix Krahmer +2
Over the last few years, debiased estimators have been proposed in order to establish rigorous confidence intervals for high-dimensional problems in machine learning and data scien…
stat.ML2023
Uncertainty quantification for learned ISTA
Frederik Hoppe, Claudio Mayrink Verdun, Felix Krahmer +2
Model-based deep learning solutions to inverse problems have attracted increasing attention in recent years as they bridge state-of-the-art numerical performance with interpretabil…