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Quantifying Aleatoric and Epistemic Uncertainty with Proper Scoring
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v1v2 (latest)

Quantifying Aleatoric and Epistemic Uncertainty with Proper Scoring Rules

18 April 2024
Paul Hofman
Yusuf Sale
Eyke Hüllermeier
    UQCVUDPER
ArXiv (abs)PDFHTML

Papers citing "Quantifying Aleatoric and Epistemic Uncertainty with Proper Scoring Rules"

10 / 10 papers shown
Epistemic Reject Option Prediction
Epistemic Reject Option Prediction
Vojtech Franc
Jakub Paplhám
80
0
0
06 Nov 2025
Uncertainty Quantification for Regression using Proper Scoring Rules
Uncertainty Quantification for Regression using Proper Scoring Rules
Alexander Fishkov
Kajetan Schweighofer
Mykyta Ielanskyi
Nikita Kotelevskii
Mohsen Guizani
Maxim Panov
UQCV
222
1
1
30 Sep 2025
Multidimensional Uncertainty Quantification via Optimal Transport
Multidimensional Uncertainty Quantification via Optimal Transport
Nikita Kotelevskii
Maiya Goloburda
Vladimir Kondratyev
Alexander Fishkov
Mohsen Guizani
Eric Moulines
Maxim Panov
179
1
0
26 Sep 2025
CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk
CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk
Ilia Azizi
Juraj Bodik
Jakob Heiss
Bin Yu
UQCVUD
405
2
0
10 Jul 2025
The Joys of Categorical Conformal Prediction
The Joys of Categorical Conformal Prediction
Michele Caprio
291
1
0
06 Jul 2025
Why Machine Learning Models Fail to Fully Capture Epistemic Uncertainty
Why Machine Learning Models Fail to Fully Capture Epistemic Uncertainty
Sebastián Jiménez
Mira Jürgens
Willem Waegeman
UDPER
692
2
0
29 May 2025
Hallucination Detection on a Budget: Efficient Bayesian Estimation of Semantic Entropy
Hallucination Detection on a Budget: Efficient Bayesian Estimation of Semantic Entropy
K. Ciosek
Nicolò Felicioni
Sina Ghiassian
362
0
0
04 Apr 2025
Probabilistic Degeneracy Detection for Point-to-Plane Error Minimization
Probabilistic Degeneracy Detection for Point-to-Plane Error MinimizationIEEE Robotics and Automation Letters (RA-L), 2024
Johan Hatleskog
Kostas Alexis
3DPC
403
9
0
14 Oct 2024
Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph
Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph
Roman Vashurin
Ekaterina Fadeeva
Artem Vazhentsev
Akim Tsvigun
Daniil Vasilev
...
Timothy Baldwin
Timothy Baldwin
Preslav Nakov
Maxim Panov
Artem Shelmanov
HILM
677
60
0
21 Jun 2024
Scalable Bayesian Learning with posteriors
Scalable Bayesian Learning with posteriors
Samuel Duffield
Kaelan Donatella
Johnathan Chiu
Phoebe Klett
Daniel Simpson
BDLUQCV
509
6
0
31 May 2024
1