Tag: epistemic
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Quantifying Epistemic Uncertainty in Diffusion Models
Quantifying Epistemic Uncertainty in Diffusion Models arXiv:2602.09170v1 Announce Type: new Abstract: To ensure high quality outputs, it is important to quantify the epistemic uncertainty of diffusion models.Existing methods are often unreliable because they mix epistemic and aleatoric uncertainty. We introduce a method based on Fisher information that explicitly isolates epistemic variance, producing more reliable plausibility…
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Frequentist Validity of Epistemic Uncertainty Estimators
Frequentist Validity of Epistemic Uncertainty Estimators arXiv:2510.22063v1 Announce Type: new Abstract: Decomposing prediction uncertainty into its aleatoric (irreducible) and epistemic (reducible) components is critical for the development and deployment of machine learning systems. A popular, principled measure for epistemic uncertainty is the mutual information between the response variable and model parameters. However, evaluating this measure…
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CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk
CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk arXiv:2507.08150v1 Announce Type: new Abstract: Accurate uncertainty quantification is critical for reliable predictive modeling, especially in regression tasks. Existing methods typically address either aleatoric uncertainty from measurement noise or epistemic uncertainty from limited data, but not necessarily both in a balanced way. We propose CLEAR, a calibration…
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Epistemic Uncertainty in Conformal Scores: A Unified Approach
Epistemic Uncertainty in Conformal Scores: A Unified Approach arXiv:2502.06995v1 Announce Type: new Abstract: Conformal prediction methods create prediction bands with distribution-free guarantees but do not explicitly capture epistemic uncertainty, which can lead to overconfident predictions in data-sparse regions. Although recent conformal scores have been developed to address this limitation, they are typically designed for specific…