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Creators/Authors contains: "Diochnos, Dimitrios I"

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  1. ; (, Proceedings of the AAAI Conference on Artificial Intelligence)
    We study the robustness of ridge regression, lasso regression, and of a neural network, when the training set has been randomly corrupted and in response to this corruption the training-size is reduced in order to remove the corrupted data. While the neural network appears to be the most robust method among these three, nevertheless lasso regression appears to be the method of choice since it suffers less loss both when the full information is available to the learner, as well as when a significant amount of the original training set has been rendered useless because of random data corruption. 
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    Full Text Available 
  2. ; ; (, IEEE)
    Accepted Manuscript Full Text Available
  3. ; ; ; ; ; ; ; ; ; ; et al (, AI Magazine)
    Abstract The NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES) focuses on creating trustworthy AI for a variety of environmental and Earth science phenomena. AI2ES includes leading experts from AI, atmospheric and ocean science, risk communication, and education, who work synergistically to develop and test trustworthy AI methods that transform our understanding and prediction of the environment. Trust is a social phenomenon, and our integration of risk communication research across AI2ES activities provides an empirical foundation for developing user‐informed, trustworthy AI. AI2ES also features activities to broaden participation and for workforce development that are fully integrated with AI2ES research on trustworthy AI, environmental science, and risk communication. 
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