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Prediction sets capture uncertainty by predicting sets of labels rather than individual labels, enabling downstream decisions to conservatively account for all plausible outcomes.
The use of confidence or fiducial limits illustrated in the case of the binomial
Charles J Clopper and Egon S Pearson · 1934
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Determination of sample sizes for setting tolerance limits
Samuel S Wilks · 1941
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Various techniques used in connection with random digits
John Von Neumann · 1951
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Nonparametric methods in statistics
Donald Alexander Stuart Fraser · 1956
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Statistical Tolerance Regions: Classical and Bayesian
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Behavioral risk factor surveillance system, 1984
Centers for Disease Control and Prevention (CDC) · 1984
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Inductive confidence machines for regression
Harris Papadopoulos, Kostas Proedrou, Volodya Vovk, and Alex Gammerman · 2002
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Adjusting the outputs of a classifier to new a priori probabilities: a simple procedure
Marco Saerens, Patrice Latinne, and Christine Decaestecker · 2002
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Monte carlo sampling methods
Alexander Shapiro · 2003
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Learning and evaluating classifiers under sample selection bias
Bianca Zadrozny · 2004
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Word sense disambiguation with distribution estimation
Yee Seng Chan and Hwee Tou Ng · 2005
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Algorithmic learning in a random world
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun · 2005
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Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira · 2006
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Correcting sample selection bias by unlabeled data
Jiayuan Huang, Arthur Gretton, Karsten Borgwardt, Bernhard Schölkopf, and Alex Smola · 2006
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Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira · 2007
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Direct importance estimation with model selection and its application to covariate shift adaptation
Masashi Sugiyama, Shinichi Nakajima, Hisashi Kashima, Paul Buenau, and Motoaki Kawanabe · 2007
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Covariate shift by kernel mean matching
Arthur Gretton, Alex Smola, Jiayuan Huang, Marcel Schmittfull, Karsten Borgwardt, and Bernhard Schölkopf · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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When training and test sets are different: characterizing learning transfer
Amos Storkey et al · 2009
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Conditional validity of inductive conformal predictors
Vladimir Vovk · 2012
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Matrix computations
Gene H Golub and Charles F Van Loan · 2013
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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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Conformal prediction under covariate shift
Ryan J Tibshirani, Rina Foygel Barber, Emmanuel Candes, and Aaditya Ramdas · 2019
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Robust validation: Confident predictions even when distributions shift
Maxime Cauchois, Suyash Gupta, Alnur Ali, and John C Duchi · 2020
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A unified view of label shift estimation
Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan, and Zachary Lipton · 2020
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Pac confidence predictions for deep neural network classifiers
Sangdon Park, Shuo Li, Insup Lee, and Osbert Bastani · 2020
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Domain adaptation under target and conditional shift
Kun Zhang, Bernhard Schölkopf, Krikamol Muandet, and Zhikun Wang · 2013
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Conformal prediction for reliable machine learning: theory, adaptations and applications
Vineeth Balasubramanian, Shen-Shyang Ho, and Vladimir Vovk · 2014
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A conformal prediction approach to explore functional data
Jing Lei, Alessandro Rinaldo, and Larry Wasserman · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Simulation and the Monte Carlo method
Reuven Y Rubinstein and Dirk P Kroese · 2016
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Classification with valid and adaptive coverage
Yaniv Romano, Matteo Sesia, and Emmanuel Candes · 2020
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Breeds: Benchmarks for subpopulation shift
Shibani Santurkar, Dimitris Tsipras, and Aleksander Madry · 2020
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A gentle introduction to conformal prediction and distribution-free uncertainty quantification
Anastasios N Angelopoulos and Stephen Bates · 2021
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Conformal inference of counterfactuals and individual treatment effects
Lihua Lei and Emmanuel J Candès · 2021
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Pac prediction sets under covariate shift
Sangdon Park, Edgar Dobriban, Insup Lee, and Osbert Bastani · 2021
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Distribution-free uncertainty quantification for classification under label shift
Aleksandr Podkopaev and Aaditya Ramdas · 2021
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Distribution-free prediction sets for two-layer hierarchical models
Robin Dunn, Larry Wasserman, and Aaditya Ramdas · 2022
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Domain adaptation under open set label shift
Saurabh Garg, Sivaraman Balakrishnan, and Zachary C Lipton · 2022
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Nih clinical center provides one of the largest publicly available chest x-ray datasets to scientific community, 2022
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Pac prediction sets for meta-learning
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Prediction sets adaptive to unknown covariate shift
Hongxiang Qiu, Edgar Dobriban, and Eric Tchetgen Tchetgen · 2022
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Rlsbench: Domain adaptation under relaxed label shift
Saurabh Garg, Nick Erickson, James Sharpnack, Alex Smola, Sivaraman Balakrishnan, and Zachary Chase Lipton · 2023
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