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cs.LG2025
A calibration test for evaluating set-based epistemic uncertainty representations
Mira Jürgens, Thomas Mortier, Eyke Hüllermeier +2
The accurate representation of epistemic uncertainty is a challenging yet essential task in machine learning. A widely used representation corresponds to convex sets of probabilist…
cs.LG2025
Chasing the Timber Trail: Machine Learning to Reveal Harvest Location Misrepresentation
Shailik Sarkar, Raquib Bin Yousuf, Linhan Wang +9
Illegal logging poses a significant threat to global biodiversity, climate stability, and depresses international prices for legal wood harvesting and responsible forest products t…