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About this paper
Dario Petri, Luca Mari
The authors argue that machine-learning uncertainty should be assessed for individual predictions, not inferred from validation performance. Drawing on metrology, they propose identifying uncertainty contributions, evaluating them using statistical or other information, and propagating them into a result-specific assessment, after checking that the model applies. This is a conceptual proposal, not a tested method or guarantee of trustworthy predictions.
Summary by alphaXiv.
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