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From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation

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arxiv 2402.10727 v3 pith:DP43ZLND submitted 2024-02-16 stat.ML cs.LG

Nikita Kotelevskii , Vladimir Kondratyev , Martin Takáč , Éric Moulines , Maxim Panov This is my paper · ORCID
classification stat.MLcs.LG
keywords uncertaintymeasurespredictivebayesiandifferentframeworkriskaleatoric
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There are various measures of predictive uncertainty in the literature, but their relationships to each other remain unclear. This paper uses a decomposition of statistical pointwise risk into components, associated with different sources of predictive uncertainty, namely aleatoric uncertainty (inherent data variability) and epistemic uncertainty (model-related uncertainty). Together with Bayesian methods, applied as an approximation, we build a framework that allows one to generate different predictive uncertainty measures. We validate our method on image datasets by evaluating its performance in detecting out-of-distribution and misclassified instances using the AUROC metric. The experimental results confirm that the measures derived from our framework are useful for the considered downstream tasks.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Evaluating Epistemic Uncertainty: Beyond OOD Detection and Active Learning

    cs.LG 2026-07 conditional novelty 6.0 of 10

    Epistemic uncertainty should be judged by how well it ranks reducible error, and a new Pareto-gap diagnostic shows proxy-task rankings can invert.

  2. Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned

    cs.CY 2025-09 conditional novelty 5.0 of 10

    The paper presents the TÜV AUSTRIA Trusted AI audit catalog, a statistical framework based on the Stochastic Application Domain Definition, minimum performance requirements, and independent-sample testing for certifyi...

  3. Position: Uncertainty Quantification Needs Reassessment for Large-language Model Agents

    cs.LG 2025-05 conditional novelty 4.0 of 10

    A position paper arguing that aleatoric/epistemic uncertainty splits fail for LLM agents and proposing underspecification, interaction, and output-based uncertainty research.

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