Robustness quantification: a new method for assessing the reliability of the predictions of a classifier
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Abstract (arXiv)
Based on existing ideas in the field of imprecise probabilities, we present a new approach for assessing the reliability of the individual predictions of a generative probabilistic classifier. We call this approach robustness quantification, compare it to uncertainty quantification, and demonstrate that it continues to work well even for classifiers that are learned from small training sets that are sampled from a shifted distribution.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 9 of its 25 references that are in the map. First posted on arXiv 28 Mar 2025.
Builds on
9 of 25 references.
References · 9
- 2020WILDS: A Benchmark of in-the-Wild Distribution Shiftscited by 1,375
- 2019Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methodscited by 913
- 2020Measuring Robustness to Natural Distribution Shifts in Image Classificationcited by 454
- 2019On Evaluating Adversarial Robustnesscited by 447
- 2018Confounding variables can degrade generalization performance of radiological deep learning modelscited by 379
- 2021Recent Advances in Adversarial Training for Adversarial Robustnesscited by 279
- 2020Aleatoric and Epistemic Uncertainty with Random Forestscited by 43
- 2024Label-wise Aleatoric and Epistemic Uncertainty Quantificationcited by 22
- 2020Towards Robust Classification with Deep Generative Forestscited by 5
Built on by
Citing papers per year: 2025: 1; 2026: 3.
4 papers.
- 2025Robustness and uncertainty: two complementary aspects of the reliability of the predictions of a classifiercited by 2
- 2026Robustness Quantification and Uncertainty Quantification: Comparing Two Methods for Assessing the Reliability of Classifier Predictionscited by 1
- 2026Robustness Quantification for Discriminative Models: a New Robustness Metric and its Application to Dynamic Classifier Selectioncited by 1
- 2026Local Robustness Quantification for Naive Bayes Classifiers and Generative Forests: a General Approachcited by 0
- 2026Local Robustness Quantification for Naive Bayes Classifiers and Generative Forests: a General Approach10 Sep 2026
- 2026Robustness Quantification for Discriminative Models: a New Robustness Metric and its Application to Dynamic Classifier Selection24 Mar 2026
- 2026Robustness Quantification and Uncertainty Quantification: Comparing Two Methods for Assessing the Reliability of Classifier Predictions24 Mar 2026
- 2025Robustness and uncertainty: two complementary aspects of the reliability of the predictions of a classifier17 Dec 2025