Nina Maria Gottschling

According to our database, Nina Maria Gottschling authored at least 8 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2026

On Hallucinations in Inverse Problems: Fundamental Limits and Provable Assessment Methods.

David Iagaru, Nina Maria Gottschling, Anders C. Hansen, Josselin Garnier

CoRR, May, 2026

2025

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

Lightning UQ Box: Uncertainty Quantification for Neural Networks.

Nils Lehmann, Nina Maria Gottschling, Jakob Gawlikowski, Adam J. Stewart, Stefan Depeweg, Eric T. Nalisnick

J. Mach. Learn. Res., 2025

2024

Uncertainty-Aware Learning With Label Noise for Glacier Mass Balance Modeling.

Codrut-Andrei Diaconu, Nina Maria Gottschling

IEEE Geosci. Remote. Sens. Lett., 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 Tropical Cyclone Wind Speed Estimation from Satellite Data.

Nils Lehmann, Nina Maria Gottschling, Stefan Depeweg, Eric T. Nalisnick

CoRR, 2024

Efficient Data Source Relevance Quantification for Multi-Source Neural Networks.

Jakob Gawlikowski, Nina Maria Gottschling

Proceedings of the 35th British Machine Vision Conference, 2024

2020

The troublesome kernel: why deep learning for inverse problems is typically unstable.

Nina Maria Gottschling, Vegard Antun, Ben Adcock, Anders C. Hansen

CoRR, 2020