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[2602.22747] Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study
Summary
This study compares set-based and distribution-based representations of epistemic uncertainty in neural networks, highlighting their relative merits through controlled empirical evaluation.
Why It Matters
Understanding how different representations of epistemic uncertainty impact machine learning models is crucial for developing more reliable AI systems. This study provides clarity on the effectiveness of these paradigms, which can influence future research and applications in uncertainty-aware machine learning.
Key Takeaways
- The study offers a controlled comparison of set-based and distribution-based representations of epistemic uncertainty.
- It evaluates the impact of representation choices on predictive accuracy across various benchmarks.
- Insights gained can guide future research in uncertainty-aware machine learning applications.
Computer Science > Machine Learning arXiv:2602.22747 (cs) [Submitted on 26 Feb 2026] Title:Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study Authors:Kaizheng Wang, Yunjia Wang, Fabio Cuzzolin, David Moens, Hans Hallez, Siu Lun Chau View a PDF of the paper titled Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study, by Kaizheng Wang and 5 other authors View PDF HTML (experimental) Abstract:Epistemic uncertainty in neural networks is commonly modeled using two second-order paradigms: distribution-based representations, which rely on posterior parameter distributions, and set-based representations based on credal sets (convex sets of probability distributions). These frameworks are often regarded as fundamentally non-comparable due to differing semantics, assumptions, and evaluation practices, leaving their relative merits unclear. Empirical comparisons are further confounded by variations in the underlying predictive models. To clarify this issue, we present a controlled comparative study enabling principled, like-for-like evaluation of the two paradigms. Both representations are constructed from the same finite collection of predictive distributions generated by a shared neural network, isolating representational effects from predictive accuracy. Our study evaluates each representation through the lens of 3 uncertainty measures across 8 benchmarks, including selective prediction and out...
