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DOE OSTI · 1733262

Uncertainty Quantification for Machine Learning

Stracuzzi, David John·Chen, Maximillian Gene·Darling, Michael Christopher·Peterson, Matthew Gregor·Vollmer, Charlie
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Abstract

In this paper, we assert the importance of uncertainty quantification for machine learning and sketch an initial research agenda. We define uncertainty in the context of machine learning, identify its sources, and motivate the importance and impact of its quantification. We then illustrate these issues with an image analysis example. The paper concludes by identifying several specific research issues and by discussing the potential long-term implications of uncertainty quantification for data analytics in general.

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97 MATHEMATICS AND COMPUTING

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Stracuzzi, David John, Chen, Maximillian Gene, Darling, Michael Christopher, Peterson, Matthew Gregor, Vollmer, Charlie. 2017-06-01. Uncertainty Quantification for Machine Learning. https://doi.org/10.2172/1733262

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