2023
Collision Probability Matching Loss for Disentangling Epistemic Uncertainty from Aleatoric Uncertainty
AISTATS 2023poster
Two important aspects of machine learning, uncertainty and calibration, have previously been studied separately. The first aspect involves knowing whether inaccuracy is due to the epistemic uncertainty of the model, which is theoretically reducible, or to the aleatoric uncertainty in the data per se…