Label-wise Aleatoric and Epistemic Uncertainty Quantification

Sale, Yusuf; Hofman, Paul; Löhr, Timo ; Wimmer, Lisa; Nagler, Thomas und Hüllermeier, Eyke (15. July 2024): Label-wise Aleatoric and Epistemic Uncertainty Quantification. 40th Conference on Uncertainty in Artificial Intelligence, Barcelona, Spain, 15. - 19. July 2024. Kiyavash, Negar und Mooij, Joris M. (eds.) : Proceedings of Machine Learning Research. Vol. 244 PMLR. pp. 3159-3179

Abstract

We present a novel approach to uncertainty quantification in classification tasks based on label-wise decomposition of uncertainty measures. This label-wise perspective allows uncertainty to be quantified at the individual class level, thereby improving cost-sensitive decision-making and helping understand the sources of uncertainty. Furthermore, it allows to define total, aleatoric, and epistemic uncertainty on the basis of non-categorical measures such as variance, going beyond common entropy-based measures. In particular, variance-based measures address some of the limitations associated with established methods that have recently been discussed in the literature. We show that our proposed measures adhere to a number of desirable properties. Through empirical evaluation on a variety of benchmark data sets – including applications in the medical domain where accurate uncertainty quantification is crucial – we establish the effectiveness of label-wise uncertainty quantification.

• Item Type: Conference or Workshop Item (Paper)
• Faculties: Mathematics, Computer Science and Statistics > Computer Science > Artificial Intelligence and Machine Learning
• Subjects: 000 Computer science, information and general works > 004 Data processing computer science