# Robustness Quantification and Uncertainty Quantification: Comparing Two Methods for Assessing the Reliability of Classifier Predictions

arXiv: https://arxiv.org/abs/2603.22988 · HTML: https://mlsanity.com/paper/2603.22988/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 24 Mar 2026.

## Builds on (most cited first, 8 of 8)

- [Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods](https://mlsanity.com/paper/1910.09457/) (2019)
- [Measuring Robustness to Natural Distribution Shifts in Image Classification](https://mlsanity.com/paper/2007.00644/) (2020)
- [On Evaluating Adversarial Robustness](https://mlsanity.com/paper/1902.06705/) (2019)
- [Recent Advances in Adversarial Training for Adversarial Robustness](https://mlsanity.com/paper/2102.01356/) (2021)
- [DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep Networks](https://mlsanity.com/paper/1906.04569/) (2019)
- [Joints in Random Forests](https://mlsanity.com/paper/2006.14937/) (2020)
- [Towards Robust Classification with Deep Generative Forests](https://mlsanity.com/paper/2007.05721/) (2020)
- [Robustness quantification: a new method for assessing the reliability of the predictions of a classifier](https://mlsanity.com/paper/2503.22418/) (2025)

## Built on by (most cited first, 1 of 1)

- [Local Robustness Quantification for Naive Bayes Classifiers and Generative Forests: a General Approach](https://mlsanity.com/paper/2609.11366/) (2026)

## 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.