4 papers indexed
Fares Fourati, Hinrich Schütze, Eyke Hüllermeier, Iryna Gurevych
The rapid progress of AI has intensified the long-standing pursuit of automation: replacing human participation with algorithms wherever possible. Implicit in this pursuit is the assumption that humans remain in the loop only because current AI systems are not yet sufficiently ca…
Jakub Paplhám, Willem Waegeman, Eyke Hüllermeier, Vojtěch Franc
Current evaluation of epistemic uncertainty relies on tasks such as out-ofdistribution detection and active learning. However, the Bayes-optimal decision strategies for these tasks do not coincide with the scores commonly used to quantify epistemic uncertainty. Building on the ep…
Joshua Stiller, Santo M. A. R. Thies, Felix Czaja, Eyke Hüllermeier
Understanding model predictions is essential for physical applications, where outputs often inform safety-critical decisions, such as structural load assessment, weather warnings, and clinical diagnosis. Shapley values satisfy many desirable properties as an attribution method, b…
Catherine George, Alireza Javanmardi, Tijana Janjić, Eyke Hüllermeier
Quantifying the evolution of uncertainty is critical to both probabilistic forecasting and data assimilation in numerical weather prediction. In this study, we investigate the applicability of conformal prediction (CP), a recent machine learning (ML) method, to quantify uncertain…