Corpus ID: 174803437

# Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift

@article{Ovadia2019CanYT,
title={Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift},
author={Y. Ovadia and E. Fertig and J. Ren and Zachary Nado and D. Sculley and Sebastian Nowozin and Joshua V. Dillon and Balaji Lakshminarayanan and Jasper Snoek},
journal={ArXiv},
year={2019},
volume={abs/1906.02530}
}
• Y. Ovadia, +6 authors Jasper Snoek
• Published 2019
• Computer Science, Mathematics
• ArXiv
• Modern machine learning methods including deep learning have achieved great success in predictive accuracy for supervised learning tasks, but may still fall short in giving useful estimates of their predictive {\em uncertainty}. Quantifying uncertainty is especially critical in real-world settings, which often involve input distributions that are shifted from the training distribution due to a variety of factors including sample bias and non-stationarity. [...] Key Result We find that traditional post-hoc…Expand Abstract
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