Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty

By Robin Senge, S. Bösner, Krzysztof Dembczynski, J. Haasenritter, O. Hirsch, N. Donner‐Banzhoff, Eyke Hüllermeier. 2014

188 citations recorded by the source corpus.

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On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products (2016, background): “Epistemic uncertainty results from the lack of knowledge that could be obtained in principle, but may be practically intractable to gather [6].”
Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models (2023, background): “In contrast, aleatoric uncertainty (data uncertainty) pertains to the irreducible component of uncertainty inherently associated with the data generation process (Senge et al., 2014).”
A statistical perspective on distillation (2021, background): “Third, the focus on approximating the Bayes probabilities p∗ suggests that the teacher’s predictions ought to primarily reflect aleatoric, rather than epistemic, uncertainty (Senge et al., 2014; Hüllermeier & Waegeman, 2021).”
Aleatoric and Epistemic Uncertainty in Machine Learning: A Tutorial Introduction (2019, methodology): “The approach by Senge et al. (2014) combines concepts from (generalized) version space learning and Bayesian inference.”