2026
CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk
ICLR 2026poster
Existing methods typically address either aleatoric uncertainty due to measurement noise or epistemic uncertainty resulting from limited data, but not both in a balanced manner. We propose CLEAR, a calibration method with two distinct parameters, $\gamma_1$ and $\gamma_2$, to combine the two uncerta…