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Standard conformal prediction methods provide a marginal coverage guarantee, which means that for a random test point, the conformal prediction set contains the true label with a user-specified probability.
Inductive confidence machines for regression
Harris Papadopoulos, Kostas Proedrou, Volodya Vovk, and Alex Gammerman · 2002
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Algorithmic Learning in a Random World
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Scikit-learn: Machine learning in Python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Édouard Duchesnay · 2011
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Conditional validity of inductive conformal predictors
Vladimir Vovk · 2012
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Applications of class-conditional conformal predictor in multi-class classification
Fan Shi, Cheng Soon Ong, and Christopher Leckie · 2013
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Distribution-free prediction bands for non-parametric regression
Jing Lei and Larry Wasserman · 2014
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Bias reduction through conditional conformal prediction
Tuve Löfström, Henrik Boström, Henrik Linusson, and Ulf Johansson · 2015
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ImageNet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Applying Mondrian cross-conformal prediction to estimate prediction confidence on large imbalanced bioactivity data sets
Jiangming Sun, Lars Carlsson, Ernst Ahlberg, Ulf Norinder, Ola Engkvist, and Hongming Chen · 2017
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Yotam Hechtlinger, Barnabás Póczos, and Larry Wasserman · 2018
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Distribution-free predictive inference for regression
Jing Lei, Max G’Sell, Alessandro Rinaldo, Ryan J. Tibshirani, and Larry Wasserman · 2018
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The iNaturalist species classification and detection dataset
Grant Van Horn, Oisin Mac Aodha, Yang Song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, and Serge Belongie · 2018
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Places: A 10 million image database for scene recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2018
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Big self-supervised models are strong semi-supervised learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey E. Hinton · 2020
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Uncertainty sets for image classifiers using conformal prediction
Anastasios N. Angelopoulos, Stephen Bates, Jitendra Malik, and Michael I. Jordan · 2021
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The limits of distribution-free conditional predictive inference
Rina Foygel Barber, Emmanuel J. Candès, Aaditya Ramdas, and Ryan J. Tibshirani · 2021
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Image-to-image regression with distribution-free uncertainty quantification and applications in imaging
Anastasios N. Angelopoulos, Amit Pal Kohli, Stephen Bates, Michael Jordan, Jitendra Malik, Thayer Alshaabi, Srigokul Upadhyayula, and Yaniv Romano · 2022
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Prediction and outlier detection in classification problems
Leying Guan and Robert Tibshirani · 2022
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Conformalized quantile regression
Yaniv Romano, Evan Patterson, and Emmanuel J. Candès · 2019
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Least ambiguous set-valued classifiers with bounded error levels
Mauricio Sadinle, Jing Lei, and Larry Wasserman · 2019
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With malice toward none: Assessing uncertainty via equalized coverage
Yaniv Romano, Rina Foygel Barber, Chiara Sabatti, and Emmanuel J. Candès
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Classification with valid and adaptive coverage
Yaniv Romano, Matteo Sesia, and Emmanuel J. Candès
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A gentle introduction to conformal prediction and distribution-free uncertainty quantification
Anastasios N. Angelopoulos and Stephen Bates · 2023
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Conformal prediction with conditional guarantees
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Localized conformal prediction: A generalized inference framework for conformal prediction
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