Uncertainty modeling/quantification and its applications in machine learning

This talk shall discuss different ways to do uncertainty modeling/quantification and its application in set-valued prediction-making, uncertainty sampling, and classification with a reject option. The notions of epistemic/aleatoric uncertainty, credal uncertainties, and probabilistic uncertainties shall be recalled, followed by discussions on their potential (dis)advantages