mlsanityData to 7 Oct 2026

Interpretable Uncertainty Quantification in AI for HEP

arXiv 2208.03284 ↗First posted 5 Aug 2022hep-ex cs.LG hep-phcited by 8

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Abstract (arXiv)

Estimating uncertainty is at the core of performing scientific measurements in HEP: a measurement is not useful without an estimate of its uncertainty. The goal of uncertainty quantification (UQ) is inextricably linked to the question, "how do we physically and statistically interpret these uncertainties?" The answer to this question depends not only on the computational task we aim to undertake, but also on the methods we use for that task. For artificial intelligence (AI) applications in HEP, there are several areas where interpretable methods for UQ are essential, including inference, simulation, and control/decision-making. There exist some methods for each of these areas, but they have not yet been demonstrated to be as trustworthy as more traditional approaches currently employed in physics (e.g., non-AI frequentist and Bayesian methods). Shedding light on the questions above requires additional understanding of the interplay of AI systems and uncertainty quantification. We briefly discuss the existing methods in each area and relate them to tasks across HEP. We then discuss recommendations for avenues to pursue to develop the necessary techniques for reliable widespread usage of AI with UQ over the next decade.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 28 of its 64 references that are in the map. First posted on arXiv 5 Aug 2022.

Builds on

28 of 64 references.

References · 28

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16 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 3; 2025: 2; 2026: 1.

8 papers.

  1. 2022Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learningcited by 10
  2. 2024Abstraction Alignment: Comparing Model-Learned and Human-Encoded Conceptual Relationshipscited by 7
  3. 2023Feature Learning and Generalization in Deep Networks with Orthogonal Weightscited by 5
  4. 2024Calibrating Bayesian Generative Machine Learning for Bayesiamplificationcited by 5
  5. 2026Uncertainty in Physics and AI: Taxonomy, Quantification, and Validationcited by 3
  6. 2024FAIR Universe HiggsML Uncertainty Dataset and Competitioncited by 2
  7. 2025Stable and Interpretable Jet Physics with IRC-Safe Equivariant Feature Extractioncited by 2
  8. 2025Uncertainty Quantification From Scaling Laws in Deep Neural Networkscited by 0