From risk to uncertainty: generating predictive uncertainty measures via bayesian estimation
Kotelevskii, Nikita Kondratyev, Vladimir Takac, Martin Moulines, Eric Panov, Maxim
Kotelevskii, Nikita
Kondratyev, Vladimir
Takac, Martin
Moulines, Eric
Panov, Maxim
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Department
Machine Learning
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Conference proceeding
Date
2025
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English
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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 an approximation, we build a framework that allows one to generate different predictive uncertainty measures. We validate our method 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. © 2025 13th International Conference on Learning Representations, ICLR 2025. All rights reserved.
Citation
N. Kotelevskii, V. Kondratyev, M. Takáč, E. Moulines, and M. Panov, “From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation,” International Conference on Representation Learning, vol. 2025, pp. 92468–92504, May 2025
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13th International Conference on Learning Representations, ICLR 2025
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13th International Conference on Learning Representations, ICLR 2025
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13th International Conference on Learning Representations, ICLR 2025
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International Conference on Learning Representations, ICLR