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cs.LG2026
Uncertainty quantification via conformal prediction in data assimilation
Catherine George, Alireza Javanmardi, Tijana Janjić +1
Quantifying the evolution of uncertainty is critical to both probabilistic forecasting and data assimilation in numerical weather prediction. In this study, we investigate the appl…
cs.LG2026
Reliable Conformal Prediction for Ordinal Classification Using the Ranked Probability Score
Stefan Haas, Luca Killmaier, Alireza Javanmardi +1
Ordinal classification (OC) arises in high-stakes domains such as medicine and finance, where uncertainty quantification must account for the severity of ordinal errors. Conformal…
cs.LG2025
Fine-Grained Uncertainty Decomposition in Large Language Models: A Spectral Approach
Nassim Walha, Sebastian G. Gruber, Thomas Decker +4
As Large Language Models (LLMs) are increasingly integrated in diverse applications, obtaining reliable measures of their predictive uncertainty has become critically important. A…