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Investigates methods to break down predictive uncertainty into components, such as aleatoric and epistemic uncertainty.
 Dynamic Programming for Epistemic Uncertainty in Markov Decision Processes Axel Benyamine Julien Grand-Clément Marek Petrik Michael I. Jordan Alain Durmus | |||
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 Uncertainty Quantification for Machine Learning: One Size Does Not Fit All Paul Hofman Yusuf Sale Eyke Hüllermeier | |||
 Credal Ensemble Distillation for Uncertainty QuantificationIEEE Annual Symposium on Foundations of Computer Science (FOCS), 2021 | |||
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 Fine-Grained Uncertainty Decomposition in Large Language Models: A Spectral ApproachIEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2025 | |||
 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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