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We study the robustness of conformal prediction, a powerful tool for uncertainty quantification, to label noise.
Conformal prediction: A gentle introduction
Anastasios N. Angelopoulos and Stephen Bates · 1935
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Regression quantiles
Roger Koenker and Gilbert Bassett · 1978
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Learning from noisy examples
Dana Angluin and Philip Laird · 1988
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On the sample complexity of noise-tolerant learning
Javed A Aslam and Scott E Decatur · 1996
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Machine-learning applications of algorithmic randomness
Vladimir Vovk, Alexander Gammerman, and Craig Saunders · 1999
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Inductive confidence machines for regression
Harris Papadopoulos, Kostas Proedrou, Vladimir Vovk, and Alex Gammerman · 2002
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Algorithmic Learning in a Random World
Vladimir Vovk, Alex Gammerman, and Glenn Shafer · 2005
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Ava: A large-scale database for aesthetic visual analysis
Naila Murray, Luca Marchesotti, and Florent Perronnin · 2012
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Classification in the presence of label noise: a survey
Benoît Frénay and Michel Verleysen · 2013
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Vision meets robotics: The kitti dataset
Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun · 2013
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Distribution-free prediction sets
Jing Lei, James Robins, and Larry Wasserman · 2013
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Microsoft COCO: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Visual aesthetic quality assessment with a regression model
Yueying Kao, Chong Wang, and Kaiqi Huang · 2015
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Cross-conformal predictors
Vladimir Vovk · 2015
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Learning deep networks from noisy labels with dropout regularization
Ishan Jindal, Matthew Nokleby, and Xuewen Chen · 2016
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Deep bilevel learning
Simon Jenni and Paolo Favaro · 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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Dimensionality-driven learning with noisy labels
Xingjun Ma, Yisen Wang, Michael E Houle, Shuo Zhou, Sarah Erfani, Shutao Xia, Sudanthi Wijewickrema, and James Bailey · 2018
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NIMA: Neural image assessment
Hossein Talebi and Peyman Milanfar · 2018
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Iterative cross learning on noisy labels
Bodi Yuan, Jianyu Chen, Weidong Zhang, Hung-Shuo Tai, and Sara McMains · 2018
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Physicochemical properties of protein tertiary structure data set
bio · 2019
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Medical expenditure panel survey, panel 19
meps_19 · 2019
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Human uncertainty makes classification more robust
Joshua C Peterson, Ruairidh M Battleday, Thomas L Griffiths, and Olga Russakovsky · 2019
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Conformalized quantile regression
Yaniv Romano, Evan Patterson, and Emmanuel Candès · 2019
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Distribution-free, risk-controlling prediction sets
Stephen Bates, Anastasios Angelopoulos, Lihua Lei, Jitendra Malik, and Michael I. Jordan · 2021
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Adversarially robust conformal prediction
Asaf Gendler, Tsui-Wei Weng, Luca Daniel, and Yaniv Romano · 2021
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Adaptive conformal inference under distribution shift
Isaac Gibbs and Emmanuel Candes · 2021
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Distribution-free uncertainty quantification for classification under label shift
Aleksandr Podkopaev and Aaditya Ramdas · 2021
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Tresnet: High performance gpu-dedicated architecture
Tal Ridnik, Hussam Lawen, Asaf Noy, Emanuel Ben Baruch, Gilad Sharir, and Itamar Friedman · 2021
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Learning to recover 3d scene shape from a single image
Wei Yin, Jianming Zhang, Oliver Wang, Simon Niklaus, Long Mai, Simon Chen, and Chunhua Shen · 2021
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Ryutaro Tanno, Ardavan Saeedi, Swami Sankaranarayanan, Daniel C Alexander, and Nathan Silberman · 2019
Cited alongside, same era.
Conformal prediction under covariate shift
Ryan J Tibshirani, Rina Foygel Barber, Emmanuel Candes, and Aaditya Ramdas · 2019
Cited alongside, same era.
L_dmi: A novel information-theoretic loss function for training deep nets robust to label noise
Yilun Xu, Peng Cao, Yuqing Kong, and Yizhou Wang · 2019
Cited alongside, same era.
Label noise types and their effects on deep learning
Görkem Algan and Ilkay Ulusoy · 2020
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Is distribution-free inference possible for binary regression?
Rina Foygel Barber · 2020
Cited alongside, same era.
Capturing human categorization of natural images by combining deep networks and cognitive models
Ruairidh M Battleday, Joshua C Peterson, and Thomas L Griffiths · 2020
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Evaluating multi-label classifiers with noisy labels
Wenting Zhao and Carla Gomes · 2021
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Predictive inference with weak supervision
Maxime Cauchois, Suyash Gupta, Alnur Ali, and John Duchi · 2022
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How many labelers do you have? a closer look at gold-standard labels
Chen Cheng, Hilal Asi, and John Duchi · 2022
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Binary classification with corrupted labels
Yonghoon Lee and Rina Foygel Barber · 2022
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Learning with noisy labels revisited: A study using real-world human annotations
Jiaheng Wei, Zhaowei Zhu, Hao Cheng, Tongliang Liu, Gang Niu, and Yang Liu · 2022
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Hurdles to artificial intelligence deployment: Noise in schemas and “gold” labels
Mohamed Abdalla and Benjamin Fine · 2023
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Conformal prediction beyond exchangeability
Rina Foygel Barber, Emmanuel J. Candès, Aaditya Ramdas, and Ryan J. Tibshirani · 2023
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Adaptive conformal classification with noisy labels
Matteo Sesia, YX Wang, and Xin Tong · 2023
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Conformal prediction under ambiguous ground truth
David Stutz, Abhijit Guha Roy, Tatiana Matejovicova, Patricia Strachan, Ali Taylan Cemgil, and Arnaud Doucet · 2023
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Conformal risk control
Anastasios Nikolas Angelopoulos, Stephen Bates, Adam Fisch, Lihua Lei, and Tal Schuster · 2024
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Non-exchangeable conformal risk control
António Farinhas, Chrysoula Zerva, Dennis Thomas Ulmer, and Andre Martins · 2024
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