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We develop a method to generate prediction intervals that have a user-specified coverage level across all regions of feature-space, a property called conditional coverage.
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Alfréd Rényi · 1959
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Regression quantiles
Roger Koenker and Gilbert Bassett · 1978
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On the Criterion that a Given System of Deviations from the Probable in the Case of a Correlated System of Variables is Such that it Can be Reasonably Supposed to have Arisen from Random Sampling
Karl Pearson · 1992
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Direct use of regression quantiles to construct confidence sets in linear models
Kenneth Q. Zhou and Stephen L. Portnoy · 1996
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Statistical inference on heteroscedastic models based on regression quantiles
Kenneth Q. Zhou and Stephen L. Portnoy · 1998
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Vladimir Ivanovich Smirnov · 2008
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Anastasios Angelopoulos, Stephen Bates, Jitendra Malik, and Michael I. Jordan · 2009
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Estimating conditional quantiles with the help of the pinball loss
Ingo Steinwart and Andreas Christmann · 2011
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Roger Koenker · 2011
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L1-penalized quantile regression in high-dimensional sparse models
Alexandre Belloni and Victor Chernozhukov · 2011
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Conditional validity of inductive conformal predictors
Vladimir Vovk · 2012
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A kernel two-sample test
Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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David Lopez-Paz, Philipp Hennig, and Bernhard Schölkopf · 2013
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Rafael Izbicki, Gilson Shimizu, and Rafael Stern · 2020
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Youngseog Chung, Willie Neiswanger, Ian Char, and Jeff Schneider · 2020
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With malice toward none: Assessing uncertainty via equalized coverage
Yaniv Romano, Rina Foygel Barber, Chiara Sabatti, and Emmanuel Candès · 2020
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A comparison of some conformal quantile regression methods
Matteo Sesia and Emmanuel J. Candès · 2020
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Adaptive, distribution-free prediction intervals for deep networks
Danijel Kivaranovic, Kory D Johnson, and Hannes Leeb · 2020
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