From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation
Abstract
There are various measures of predictive uncertainty in the literature, but their relationships to each other remain unclear. This paper uses a decomposition of statistical pointwise risk into components associated with different sources of predictive uncertainty: namely, aleatoric uncertainty (inherent data variability) and epistemic uncertainty (model-related uncertainty). Together with Bayesian methods applied as approximations, we build a framework that allows one to generate different predictive uncertainty measures. We validate measures, derived from our framework on image datasets by evaluating its performance in detecting out-of-distribution and misclassified instances using the AUROC metric. The experimental results confirm that the measures derived from our framework are useful for the considered downstream tasks.
Cite
Text
Kotelevskii et al. "From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation." International Conference on Learning Representations, 2025.Markdown
[Kotelevskii et al. "From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation." International Conference on Learning Representations, 2025.](https://mlanthology.org/iclr/2025/kotelevskii2025iclr-risk/)BibTeX
@inproceedings{kotelevskii2025iclr-risk,
title = {{From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation}},
author = {Kotelevskii, Nikita and Kondratyev, Vladimir and Takáč, Martin and Moulines, Eric and Panov, Maxim},
booktitle = {International Conference on Learning Representations},
year = {2025},
url = {https://mlanthology.org/iclr/2025/kotelevskii2025iclr-risk/}
}