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While improving prediction accuracy has been the focus of machine learning in recent years, this alone does not suffice for reliable decision-making.
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Abraham Wald · 1943
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“The Theory of Probabilities”
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Wassily Hoeffding · 1963
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Iosif Pinelis and S. Utev · 1989
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“Machine-learning applications of algorithmic randomness”
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“Interval estimation for a binomial proportion”
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“Inductive confidence machines for regression”
Harris Papadopoulos, Kostas Proedrou, Vladimir Vovk and Alex Gammerman · 2002
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“On Hoeffding’s inequalities”
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“Efficient morphological reconstruction: A downhill filter”
Kevin Robinson and Paul Whelan · 2004
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“Algorithmic Learning in a Random World”
Vladimir Vovk, Alex Gammerman and Glenn Shafer · 2005
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“Concentration inequalities for functions of independent variables”
Andreas Maurer · 2006
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“Ranking and empirical minimization of U-statistics”
Stephan Clemencon, Gabor Lugosi and Nicolas Vayatis · 2008
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“Statistical Tolerance Regions: Theory, Applications, and Computation”
K. Krishnamoorthy and T. Mathew · 2009
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“Learning nondeterministic classifiers”
Juané del Coz, Jorge Díez and Antonio Bahamonde · 2009
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“Empirical Bernstein bounds and sample variance penalization”
Andreas Maurer and Massimiliano Pontil · 2009
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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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“Torchvision: The machine-vision package of Torch”
Sébastien Marcel and Yann Rodriguez · 2010
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“Conditional validity of inductive conformal predictors”
Vladimir Vovk · 2012
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“WordNet”
Christiane Fellbaum · 2012
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“Hedging your bets: Optimizing accuracy-specificity trade-offs in large scale visual recognition”
Jia Deng, Jonathan Krause, Alexander Berg and Li Fei-Fei · 2012
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“Conformal prediction under covariate shift”
Ryan Tibshirani, Rina Foygel, Emmanuel Candes and Aaditya Ramdas · 2019
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“Unlabeled data improves adversarial robustness”
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John Duchi and Percy Liang · 2019
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“PAC confidence sets for deep neural networks via calibrated prediction”
Sangdon Park, Osbert Bastani, Nikolai Matni and Insup Lee · 2020
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“The limits of distribution-free conditional predictive inference”
Rina Barber, Emmanuel Candès, Aaditya Ramdas and Ryan Tibshirani · 2020
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“Classification with valid and adaptive coverage”
Yaniv Romano, Matteo Sesia and Emmanuel Candès · 2020
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“Towards automatic polyp detection with a polyp appearance model”
Jorge Bernal, Javier Sánchez and Fernando Vilarino · 2012
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“Classification with confidence”
Jing Lei · 2014
Cited alongside, same era.
“Microsoft COCO: Common objects in context”
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár and C Zitnick · 2014
Cited alongside, same era.
“Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer”
Juan Silva, Aymeric Histace, Olivier Romain, Xavier Dray and Bertrand Granado · 2014
Cited alongside, same era.
“Structural insights into enzymatic activity and substrate specificity determination by a single amino acid in nitrilase from Syechocystis sp. PCC6803”
Lujia Zhang, Bo Yin, Chao Wang, Shuiqin Jiang, Hualei Wang, Y Yuan and Dongzhi Wei · 2014
Cited alongside, same era.
“A conformal prediction approach to explore functional data”
Jing Lei, Alessandro Rinaldo and Larry Wasserman · 2015
Cited alongside, same era.
Leying Guan · 2020
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“Conformal calibrators”
Vladimir Vovk, Ivan Petej, Paolo Toccaceli, Alexander Gammerman, Ernst Ahlberg and Lars Carlsson · 2020
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“Conformal inference of counterfactuals and individual treatment effects”
Lihua Lei and Emmanuel. Candès · 2020
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“Robust validation: Confident predictions even when distributions shift”
Maxime Cauchois, Suyash Gupta, Alnur Ali and John. Duchi · 2020
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“A distribution-free test of covariate shift using conformal prediction”
Xiaoyu Hu and Jing Lei · 2020
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“Efficient set-valued prediction in multi-class classification”
Thomas Mortier, Marek Wydmuch, Krzysztof Dembczyński, Eyke Hüllermeier and Willem Waegeman · 2020
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“Nested conformal prediction and quantile out-of-bag ensemble methods”
Chirag Gupta, Arun. Kuchibhotla and Aaditya. Ramdas · 2020
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“Variance-adaptive confidence sequences by betting”
Ian Waudby-Smith and Aaditya Ramdas · 2020
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“TResNet: High performance GPU-dedicated architecture”
Tal Ridnik, Hussam Lawen, Asaf Noy and Itamar Friedman · 2020
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“HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy”
Hanna Borgli, Vajira Thambawita, Pia Smedsrud, Steven Hicks, Debesh Jha, Sigrun Eskeland, Kristin Randel, Konstantin Pogorelov, Mathias Lux and Duc Nguyen · 2020
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“Pranet: Parallel reverse attention network for polyp segmentation”
Deng-Ping Fan, Ge-Peng Ji, Tao Zhou, Geng Chen, Huazhu Fu, Jianbing Shen and Ling Shao · 2020
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“PAC confidence predictions for deep neural network classifiers”
Sangdon Park, Shuo Li, Osbert Bastani and Insup Lee · 2021
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“Predictive inference with the jackknife+”
Rina Barber, Emmanuel Candès, Aaditya Ramdas and Ryan Tibshirani · 2021
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“Knowing what you know: Valid and validated confidence sets in multiclass and multilabel prediction”
Maxime Cauchois, Suyash Gupta and John. Duchi · 2021
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“Uncertainty sets for image classifiers using conformal prediction”
Anastasios Angelopoulos, Stephen Bates, Jitendra Malik and Michael Jordan · 2021
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