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Tracing sources of epistemic uncertainty in deep learning predictions: homo- and hetero-scedastic linearized estimators

arXiv 2608.07630 ↗First posted 7 Aug 2026cs.LG stat.MLcited by 0

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Abstract (arXiv)

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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References · 23

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11 more
  1. 2023Studying Large Language Model Generalization with Influence Functionscited by 326
  2. 2020Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernelcited by 187
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