The troublesome kernel: why deep learning for inverse problems is typically unstable. - Nina Maria Gottschling; Vegard Antun; Ben Adcock; Anders C. Hansen (CoRR, 2020)

Efficient Data Source Relevance Quantification for Multi-Source Neural Networks. - Jakob Gawlikowski; Nina Maria Gottschling (BMVC, 2024)

Uncertainty Aware Tropical Cyclone Wind Speed Estimation from Satellite Data. - Nils Lehmann; Nina Maria Gottschling; Stefan Depeweg; Eric T. Nalisnick (CoRR, 2024)

Lightning UQ Box: A Comprehensive Framework for Uncertainty Quantification in Deep Learning. - Nils Lehmann; Jakob Gawlikowski; Adam J. Stewart; Vytautas Jancauskas; Stefan Depeweg; Eric T. Nalisnick; Nina Maria Gottschling (CoRR, 2024)

Uncertainty-Aware Learning With Label Noise for Glacier Mass Balance Modeling. - Codrut-Andrei Diaconu; Nina Maria Gottschling (IEEE Geosci. Remote. Sens. Lett., 2024)

The Troublesome Kernel: On Hallucinations, No Free Lunches, and the Accuracy-Stability Tradeoff in Inverse Problems. - Nina Maria Gottschling; Vegard Antun; Anders C. Hansen; Ben Adcock (SIAM Rev., 2025)