Research Blog
12.11.2026
One Toolbox for Many Kinds of Uncertainty
MCML Research Insight – With Paul Hofman, Clemens Damke, Timo Löhr, Santo Thies, Alireza Javanmardi, Valentin Margraf, Yusuf Sale, Eyke Hüllermeier, and Maximilian Muschalik
Most machine learning models return a prediction but say little about what they don’t know. The Python package probly turns existing models into uncertainty-aware ones and brings many ways of …
29.10.2026
Teaching Language Models to Express Uncertainty
MCML Research Insight – With David Bani-Harouni, Chantal Pellegrini, Ege Özsoy, Kamilia Zaripova, Nassir Navab, and Matthias Keicher
An AI assistant may give a confident answer even when it is wrong. But what if it could tell us how sure it really is? MCML researchers have developed a reinforcement learning approach that teaches …
15.10.2026
When Flatter Is Not Always Better
MCML Research Insight – With Leonardo Galli, Wiebke Bartolomaeus, and Holger Rauhut
Imagine hiking through a mountain landscape. A broad, flat valley may look like the easiest route. Yet the flattest path does not necessarily lead where you want to go. A similar idea appears in the …
2024-11-22 - Last modified: 2026-11-12