# Robustness and uncertainty: two complementary aspects of the reliability of the predictions of a classifier

arXiv: https://arxiv.org/abs/2512.15492 · HTML: https://mlsanity.com/paper/2512.15492/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 17 Dec 2025.

## Builds on (most cited first, 7 of 7)

- [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)
- [Aleatoric and Epistemic Uncertainty with Random Forests](https://mlsanity.com/paper/2001.00893/) (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, 2 of 2)

- [Robustness Quantification for Discriminative Models: a New Robustness Metric and its Application to Dynamic Classifier Selection](https://mlsanity.com/paper/2603.23318/) (2026)
- [Robustness Meets Uncertainty: Evidential Adversarial Training for Robust Selective Classification](https://mlsanity.com/paper/2607.03075/) (2026)

## Abstract (arXiv)

We consider two conceptually different approaches for assessing the reliability of the individual predictions of a classifier: Robustness Quantification (RQ) and Uncertainty Quantification (UQ). We compare both approaches on a number of benchmark datasets and show that there is no clear winner between the two, but that they are complementary and can be combined to obtain a hybrid approach that outperforms both RQ and UQ. As a byproduct of our approach, for each dataset, we also obtain an assessment of the relative importance of uncertainty and robustness as sources of unreliability.