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Conformal prediction is a powerful distribution-free tool for uncertainty quantification, establishing valid prediction intervals with finite-sample guarantees.
Algorithmic learning in a random world
Vladimir Vovk, Alexander Gammerman, and Glenn Shafer · 2005
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Regression quantiles
Roger Koenker and Gilbert Bassett Jr · 2005
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UCI machine learning repository, 2007
Arthur Asuncion and David Newman · 2007
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A tutorial on conformal prediction
Glenn Shafer and Vladimir Vovk · 2008
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Inductive conformal prediction: Theory and application to neural networks
Harris Papadopoulos · 2008
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Normalized nonconformity measures for regression conformal prediction
Harris Papadopoulos, Alex Gammerman, and Volodya Vovk · 2008
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Neural networks regression inductive conformal predictor and its application to total electron content prediction
Harris Papadopoulos and Haris Haralambous · 2010
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Regression conformal prediction with nearest neighbours
Harris Papadopoulos, Vladimir Vovk, and Alexander Gammerman · 2011
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Conditional validity of inductive conformal predictors
Vladimir Vovk · 2012
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Efficient conformal regressors using bagged neural nets
Ulf Johansson, Cecilia Sönströd, and Henrik Linusson · 2015
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Efficient conformal regressors using bagged neural nets
Ulf Johansson, Cecilia Sönströd, and Henrik Linusson · 2015
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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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Conformalized quantile regression
Yaniv Romano, Evan Patterson, and Emmanuel Candes · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova · 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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Flexible distribution-free conditional predictive bands using density estimators
Rafael Izbicki, Gilson T Shimizu, and Rafael B Stern · 2020
VIME: Extending the success of self-and semi-supervised learning to tabular domain
Jinsung Yoon, Yao Zhang, James Jordon, and Mihaela van der Schaar · 2020
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Uncertainty prediction for deep sequential regression using meta models
Jiri Navratil, Matthew Arnold, and Benjamin Elder · 2020
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AutoCP: Automated pipelines for accurate prediction intervals
Yao Zhang, William Zame, and Mihaela van der Schaar · 2020
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The limits of distribution-free conditional predictive inference
Rina Foygel Barber, Emmanuel J Candes, Aaditya Ramdas, and Ryan J Tibshirani · 2021
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Distributional conformal prediction
Victor Chernozhukov, Kaspar Wüthrich, and Yinchu Zhu · 2021
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A simple framework for contrastive learning of visual representations
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Bootstrap your own latent A new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Conformal prediction using conditional histograms
Matteo Sesia and Yaniv Romano · 2021
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Self-supervision enhanced feature selection with correlated gates
Changhee Lee, Fergus Imrie, and Mihaela van der Schaar · 2022
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Data-suite: Data-centric identification of in-distribution incongruous examples
Nabeel Seedat, Jonathan Crabbé, and Mihaela van der Schaar · 2022
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Predictive inference with feature conformal prediction
Jiaye Teng, Chuan Wen, Dinghuai Zhang, Yoshua Bengio, Yang Gao, and Yang Yuan · 2022
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