Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
Copyright policy )Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
AbstractThe 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.
- Ghent University Belgium
- Ludwig-Maximilians-Universität München Germany
- University of Paderborn Germany
FOS: Computer and information sciences, Computer Science - Machine Learning, Epistemic uncertainty, generative models, probability, epistemic uncertainty, Bayesian inference, Learning and adaptive systems in artificial intelligence, Gaussian processes, Likelihood-based methods, ensembles, Machine Learning (stat.ML), Machine Learning (cs.LG), conformal prediction, Statistics - Machine Learning, Deep neural networks, set-valued prediction, Set-valued prediction, Ensembles, uncertainty, Probability, version space learning, Uncertainty, Generative models, calibration, Reasoning under uncertainty in the context of artificial intelligence, Mathematics and Statistics, Credal sets and classifiers, deep neural networks, credal sets and classifiers, Version space learning, Calibration, Conformal prediction, likelihood-based methods
FOS: Computer and information sciences, Computer Science - Machine Learning, Epistemic uncertainty, generative models, probability, epistemic uncertainty, Bayesian inference, Learning and adaptive systems in artificial intelligence, Gaussian processes, Likelihood-based methods, ensembles, Machine Learning (stat.ML), Machine Learning (cs.LG), conformal prediction, Statistics - Machine Learning, Deep neural networks, set-valued prediction, Set-valued prediction, Ensembles, uncertainty, Probability, version space learning, Uncertainty, Generative models, calibration, Reasoning under uncertainty in the context of artificial intelligence, Mathematics and Statistics, Credal sets and classifiers, deep neural networks, credal sets and classifiers, Version space learning, Calibration, Conformal prediction, likelihood-based methods
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