Investigates methods to break down predictive uncertainty into components, such as aleatoric and epistemic uncertainty.
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 Measuring Aleatoric and Epistemic Uncertainty in LLMs: Empirical Evaluation on ID and OOD QA Tasks Kevin Wang Subre Abdoul Moktar Jia Li Kangshuo Li Feng Chen | |||
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 Robust Explanations Through Uncertainty Decomposition: A Path to Trustworthier AI Chenrui Zhu Louenas Bounia Vu Linh Nguyen Sébastien Destercke Arthur Hoarau | |||
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 Aleatoric and Epistemic Uncertainty Measures for Ordinal Classification through Binary Reduction Stefan Haas Eyke Hüllermeier | |||
 Uncertainty in Repeated Implicit Feedback as a Measure of ReliabilityUser Modeling, Adaptation, and Personalization (UMAP), 2025 | |||
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 On Information-Theoretic Measures of Predictive UncertaintyConference on Uncertainty in Artificial Intelligence (UAI), 2024 | |||
 (Un)certainty of (Un)fairness: Preference-Based Selection of Certainly
Fair Decision-MakersEuropean Conference on Artificial Intelligence (ECAI), 2024 Manh Khoi Duong Stefan Conrad | |||
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