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We study three notions of uncertainty quantification -- calibration, confidence intervals and prediction sets -- for binary classification in the distribution-free setting, that is without making any distributional assumptions on the data.
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Christopher AT Ferro and Thomas E Fricker · 2012
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Vladimir Vovk and Ivan Petej · 2014
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Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich · 2019
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Ananya Kumar, Percy S Liang, and Tengyu Ma · 2019
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Learning for single-shot confidence calibration in deep neural networks through stochastic inferences
Seonguk Seo, Paul Hongsuck Seo, and Bohyung Han · 2019
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Conformal prediction under covariate shift
Ryan J Tibshirani, Rina Foygel Barber, Emmanuel Candes, and Aaditya Ramdas · 2019
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Gia-Lac Tran, Edwin V Bonilla, John Cunningham, Pietro Michiardi, and Maurizio Filippone · 2019
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Juozas Vaicenavicius, David Widmann, Carl Andersson, Fredrik Lindsten, Jacob Roll, and Thomas B Schön · 2019
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