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Pitfalls of Epistemic Uncertainty Quantification Through Loss Minimisation
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Second-order uncertainty prediction, a distribution of distributions, has recently been treated using inference based on Bayesian methods and credal sets.
“This type of prediction, a distribution of distributions, also called second-order uncertainty (Hüllermeier et al., 2022;Bengs et al., 2022), has been recently treated using inference based on Bayesian methods and credal sets, which are convex sets of probability distributions (Shaker & Hüllermeier, 2021).”
From Classifier calibration: a survey on how to assess and improve predicted class probabilities (2023) · cites this paper as Bengs et al., 2022
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Metadata
- Authors
- Bengs, V., Hüllermeier, E., & Waegeman, W.
- Venue
- Advances in Neural Information Processing Systems 35
- Published
- 2022
- Pages
- 29205-29216
- ISSN
- 1049-5258 (ISSN-L)
- OpenAlex
- W7133199676
- Cited by
- 15
- References
- 0
- Citations by year
- 2026: 11 · 2025: 1 · 2024: 1 · 2023: 2
- FWCI
- 3.94
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- neural information processing systems (publishedVersion)
- Topics
- Physical Sciences › Engineering › Computational Mechanics › Probabilistic and Robust Engineering Design
- Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Keywords
- epistemic uncertainty quantification, loss reduction, Bayesian inference, model misspecification
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