Do you understand epistemic uncertainty? Think again! Rigorous frequentist epistemic uncertainty estimation in regression

• URL: http://arxiv.org/abs/2503.13317v1
• Date: Mon, 17 Mar 2025 15:54:57 GMT
• Title: Do you understand epistemic uncertainty? Think again! Rigorous frequentist epistemic uncertainty estimation in regression
• Authors: Enrico Foglia, Benjamin Bobbia, Nikita Durasov, Michael Bauerheim, Pascal Fua, Stephane Moreau, Thierry Jardin,
• Abstract summary: We train models to generate conditional predictions by feeding their initial output back as an additional input.<n>This method allows for a rigorous measurement of model uncertainty by observing how prediction responses change when conditioned on the model's previous answer.
• Score: 37.422257886583424
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantifying model uncertainty is critical for understanding prediction reliability, yet distinguishing between aleatoric and epistemic uncertainty remains challenging. We extend recent work from classification to regression to provide a novel frequentist approach to epistemic and aleatoric uncertainty estimation. We train models to generate conditional predictions by feeding their initial output back as an additional input. This method allows for a rigorous measurement of model uncertainty by observing how prediction responses change when conditioned on the model's previous answer. We provide a complete theoretical framework to analyze epistemic uncertainty in regression in a frequentist way, and explain how it can be exploited in practice to gauge a model's uncertainty, with minimal changes to the original architecture.

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