Robustness Quantification and Uncertainty Quantification: Comparing Two Methods for Assessing the Reliability of Classifier Predictions
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Abstract (arXiv)
We consider two approaches for assessing the reliability of the individual predictions of a classifier: Robustness Quantification (RQ) and Uncertainty Quantification (UQ). We explain the conceptual differences between the two approaches, compare both approaches on a number of benchmark datasets and show that RQ is capable of outperforming UQ, both in a standard setting and in the presence of distribution shift. Beside showing that RQ can be competitive with UQ, we also demonstrate the complementarity of RQ and UQ by showing that a combination of both approaches can lead to even better reliability assessments.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 18 references that are in the map. First posted on arXiv 24 Mar 2026.
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8 of 18 references.
References · 8
- 2019Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methodscited by 903
- 2020Measuring Robustness to Natural Distribution Shifts in Image Classificationcited by 452
- 2019On Evaluating Adversarial Robustnesscited by 442
- 2021Recent Advances in Adversarial Training for Adversarial Robustnesscited by 276
- 2019DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep Networkscited by 92
- 2020Joints in Random Forestscited by 21
- 2020Towards Robust Classification with Deep Generative Forestscited by 5
- 2025Robustness quantification: a new method for assessing the reliability of the predictions of a classifiercited by 4
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Citing papers per year: 2026: 1.
1 paper.