no code implementations • 14 Feb 2024 • Mira Jürgens, Nis Meinert, Viktor Bengs, Eyke Hüllermeier, Willem Waegeman
Trustworthy ML systems should not only return accurate predictions, but also a reliable representation of their uncertainty.
no code implementations • 22 Nov 2022 • Kenza Tazi, Emiliano Díaz Salas-Porras, Ashwin Braude, Daniel Okoh, Kara D. Lamb, Duncan Watson-Parris, Paula Harder, Nis Meinert
The pipeline's first two components, a pyroCb database and a pyroCb forecast model, are presented.
no code implementations • 16 Nov 2022 • Emiliano Díaz Salas-Porras, Kenza Tazi, Ashwin Braude, Daniel Okoh, Kara D. Lamb, Duncan Watson-Parris, Paula Harder, Nis Meinert
A first causal discovery analysis from observational data of pyroCb (storm clouds generated from extreme wildfires) is presented.
2 code implementations • 20 May 2022 • Nis Meinert, Jakob Gawlikowski, Alexander Lavin
There is a significant need for principled uncertainty reasoning in machine learning systems as they are increasingly deployed in safety-critical domains.
1 code implementation • 13 Apr 2021 • Nis Meinert, Alexander Lavin
We discuss three issues with a proposed solution to extract aleatoric and epistemic uncertainties from regression-based neural networks.