Abstract

Due to safety requirements in practical applications, the notion of uncertainty has recently received increasing attention in machine learning research. This talk will address questions regarding the representation and adequate handling of (predictive) uncertainty in (supervised) machine learning. A particular focus will be put on the distinction between two important types of uncertainty, often referred to as aleatoric and epistemic, and how to quantify these uncertainties in terms of appropriate numerical measures. Roughly speaking, while aleatoric uncertainty is due to the randomness inherent in the data generating process, epistemic uncertainty is caused by the learner’s ignorance of the true underlying model. Some conceptual and theoretical issues of existing methods will identified, showing the challenging nature of uncertainty quantification in general and the disentanglement of aleatoric and epistemic uncertainty in particular.