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Aleatoric and epistemic uncertainty in machine learning : an introduction to concepts and methods

Eyke Huellermeier and Willem Waegeman (UGent)
(2021) MACHINE LEARNING. 110(3). p.457-506
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Abstract
The notion of uncertainty is of major importance in machine learning and constitutes a key element of machine learning methodology. In line with the statistical tradition, uncertainty has long been perceived as almost synonymous with standard probability and probabilistic predictions. Yet, due to the steadily increasing relevance of machine learning for practical applications and related issues such as safety requirements, new problems and challenges have recently been identified by machine learning scholars, and these problems may call for new methodological developments. In particular, this includes the importance of distinguishing between (at least) two different types of uncertainty, often referred to as aleatoric and epistemic. In this paper, we provide an introduction to the topic of uncertainty in machine learning as well as an overview of attempts so far at handling uncertainty in general and formalizing this distinction in particular.
Keywords
Uncertainty, Probability, Epistemic uncertainty, Version space learning, Bayesian inference, Calibration, Ensembles, Gaussian processes, Deep neural networks, Likelihood-based methods, Credal sets and classifiers, Conformal prediction, Set-valued prediction, Generative models

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MLA
Huellermeier, Eyke, and Willem Waegeman. “Aleatoric and Epistemic Uncertainty in Machine Learning : An Introduction to Concepts and Methods.” MACHINE LEARNING, vol. 110, no. 3, 2021, pp. 457–506, doi:10.1007/s10994-021-05946-3.
APA
Huellermeier, E., & Waegeman, W. (2021). Aleatoric and epistemic uncertainty in machine learning : an introduction to concepts and methods. MACHINE LEARNING, 110(3), 457–506. https://doi.org/10.1007/s10994-021-05946-3
Chicago author-date
Huellermeier, Eyke, and Willem Waegeman. 2021. “Aleatoric and Epistemic Uncertainty in Machine Learning : An Introduction to Concepts and Methods.” MACHINE LEARNING 110 (3): 457–506. https://doi.org/10.1007/s10994-021-05946-3.
Chicago author-date (all authors)
Huellermeier, Eyke, and Willem Waegeman. 2021. “Aleatoric and Epistemic Uncertainty in Machine Learning : An Introduction to Concepts and Methods.” MACHINE LEARNING 110 (3): 457–506. doi:10.1007/s10994-021-05946-3.
Vancouver
1.
Huellermeier E, Waegeman W. Aleatoric and epistemic uncertainty in machine learning : an introduction to concepts and methods. MACHINE LEARNING. 2021;110(3):457–506.
IEEE
[1]
E. Huellermeier and W. Waegeman, “Aleatoric and epistemic uncertainty in machine learning : an introduction to concepts and methods,” MACHINE LEARNING, vol. 110, no. 3, pp. 457–506, 2021.
@article{8703853,
  abstract     = {{The notion of uncertainty is of major importance in machine learning and constitutes a key element of machine learning methodology. In line with the statistical tradition, uncertainty has long been perceived as almost synonymous with standard probability and probabilistic predictions. Yet, due to the steadily increasing relevance of machine learning for practical applications and related issues such as safety requirements, new problems and challenges have recently been identified by machine learning scholars, and these problems may call for new methodological developments. In particular, this includes the importance of distinguishing between (at least) two different types of uncertainty, often referred to as aleatoric and epistemic. In this paper, we provide an introduction to the topic of uncertainty in machine learning as well as an overview of attempts so far at handling uncertainty in general and formalizing this distinction in particular.}},
  author       = {{Huellermeier, Eyke and Waegeman, Willem}},
  issn         = {{0885-6125}},
  journal      = {{MACHINE LEARNING}},
  keywords     = {{Uncertainty,Probability,Epistemic uncertainty,Version space learning,Bayesian inference,Calibration,Ensembles,Gaussian processes,Deep neural networks,Likelihood-based methods,Credal sets and classifiers,Conformal prediction,Set-valued prediction,Generative models}},
  language     = {{eng}},
  number       = {{3}},
  pages        = {{457--506}},
  title        = {{Aleatoric and epistemic uncertainty in machine learning : an introduction to concepts and methods}},
  url          = {{http://doi.org/10.1007/s10994-021-05946-3}},
  volume       = {{110}},
  year         = {{2021}},
}
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