Tracing sources of epistemic uncertainty in deep learning predictions: homo- and hetero-scedastic linearized estimators
arXiv:2608.07630v1 Announce Type: new Abstract: We adapt two classical statistical estimators for quantifying uncertainty to modern deep learning, in order to provide clearer insights into uncertainty attributable to two sources : aleatoric uncertainty, or locally scarce data. Our approach leverages recent advances in approximate Fisher Information Matrices, to enable scaling to actual architectures. Experimental results demonstrate how each test points is differentially impacted by both sources, highlighting the practical utility of our estimators in improving the robustness of real-world applications.
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[Submitted on 7 Aug 2026]
Title:Tracing sources of epistemic uncertainty in deep learning predictions: homo- and hetero-scedastic linearized estimators
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Abstract:We adapt two classical statistical estimators for quantifying uncertainty to modern deep learning, in order to provide clearer insights into uncertainty attributable to two sources : aleatoric uncertainty, or locally scarce data. Our approach leverages recent advances in approximate Fisher Information Matrices, to enable scaling to actual architectures. Experimental results demonstrate how each test points is differentially impacted by both sources, highlighting the practical utility of our estimators in improving the robustness of real-world applications.
Subjects:
Machine Learning (cs.LG); Machine Learning (stat.ML)
Cite as: arXiv:2608.07630 [cs.LG]
(or arXiv:2608.07630v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.07630
arXiv-issued DOI via DataCite
Submission history
From: Thomas George [view email] [v1] Fri, 7 Aug 2026 12:18:52 UTC (9,181 KB)
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