2026
Position: Epistemic uncertainty estimation methods are fundamentally incomplete
ICML 2026poster
Identifying and disentangling sources of predictive uncertainty is essential for trustworthy supervised learning. We argue that widely used second-order decomposition-based approaches to uncertainty quantification are fundamentally incomplete. First, we show that unaccounted bias contaminates uncert…