From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866929716313718784 |
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| author | Kotelevskii, Nikita Kondratyev, Vladimir Takáč, Martin Moulines, Éric Panov, Maxim |
| author_facet | Kotelevskii, Nikita Kondratyev, Vladimir Takáč, Martin Moulines, Éric Panov, Maxim |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_10727 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation Kotelevskii, Nikita Kondratyev, Vladimir Takáč, Martin Moulines, Éric Panov, Maxim Machine Learning 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. |
| title | From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2402.10727 |