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20222026
most citedQuantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures?

11 citations · 14 across the 9 of their papers we have counts for

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cs.LG2024

Label-wise Aleatoric and Epistemic Uncertainty Quantification

Yusuf Sale, Paul Hofman, Timo Löhr +3

We present a novel approach to uncertainty quantification in classification tasks based on label-wise decomposition of uncertainty measures. This label-wise perspective allows unce…

cs.LG2024★ 2 cited

Quantifying Aleatoric and Epistemic Uncertainty with Proper Scoring Rules

Paul Hofman, Yusuf Sale, Eyke Hüllermeier

Uncertainty representation and quantification are paramount in machine learning and constitute an important prerequisite for safety-critical applications. In this paper, we propose…