Uncertainty Quantification in Machine Learning: From Aleatoric to Epistemic

Uncertainty Quantification in Machine Learning: From Aleatoric to Epistemic

Colloquium

Abstract: Due to the growing relevance of machine learning for real-world applications, many of which are coming with safety requirements, the notion of uncertainty has received increasing attention in machine learning research in the recent past. This talk will address questions regarding the adequate representation and quantification of (predictive) uncertainty in machine learning and elaborate on the distinction between two important types of uncertainty, often referred to as aleatoric and epistemic. Roughly speaking, while aleatoric uncertainty is due to the randomness inherent in the data-generating process, epistemic uncertainty is caused by the learner's lack of knowledge about this process. Bayesian methods are commonly used to quantify both types of uncertainty, but alternative approaches have become popular in recent years, notably so-called evidential deep learning methods that are based on the idea of second-order loss minimisation. By exploring some conceptual and theoretical issues of such approaches, the challenging nature of quantifying epistemic uncertainty will be highlighted.

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