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Label shift refers to the phenomenon where the prior class probability p(y) changes between the training and test distributions, while the conditional probability p(x|y) stays fixed.
The comparison and evaluation of forecasters
Morris H DeGroot and Stephen E Fienberg · 1983
Earlier work this paper cites.
Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
John C Platt · 1999
Earlier work this paper cites.
Adjusting the outputs of a classifier to new a priori probabilities: a simple procedure
Marco Saerens, Patrice Latinne, and Christine Decaestecker · 2002
Earlier work this paper cites.
Transforming classifier scores into accurate multiclass probability estimates
Bianca Zadrozny and Charles Elkan · 2002
Earlier work this paper cites.
Predicting good probabilities with supervised learning
Alexandru Niculescu-Mizil and Rich Caruana · 2005
Earlier work this paper cites.
When training and test sets are different: Characterizing learning transfer
Storkey Amos · 2008
Earlier work this paper cites.
On causal and anticausal learning
Bernhard Schoelkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, and Joris Mooij · 2012
Earlier work this paper cites.
Domain adaptation under target and conditional shift
Kun Zhang, Bernhard Schölkopf, Krikamol Muandet, and Zhikun Wang · 2013
Cited alongside, same era.
Kaggle competition on diabetic retinopathy detection
Kaggle · 2015
Cited alongside, same era.
Calibrated structured prediction
Volodymyr Kuleshov and Percy S Liang · 2015
Cited alongside, same era.
Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht · 2015
Cited alongside, same era.
Reliable confidence estimation via online learning
Volodymyr Kuleshov and Stefano Ermon · 2016
Cited alongside, same era.
Selective Classification for Deep Neural Networks
Y. Geifman and R. El-Yaniv · 2017
Cited alongside, same era.
Detecting and correcting for label shift with black box predictors
Zachary Lipton, Yu-Xiang Wang, and Alexander Smola · 2018
Later among the works it cites.
Regularized learning for domain adaptation under label shifts
Kamyar Azizzadenesheli, Anqi Liu, Fanny Yang, and Animashree Anandkumar · 2019
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What is the effect of importance weighting in deep learning?
Jonathon Byrd and Zachary Lipton · 2019
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Jeffreydf/kaggle_diabetic_retinopathy: Fifth place solution of the kaggle diabetic retinopathy competition
Jeffrey De Fauw · 2019
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Evaluating model calibration in classification
Juozas Vaicenavicius, David Widmann, Carl Andersson, Fredrik Lindsten, Jacob Roll, and Thomas B Schön · 2019
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger · 2017
Cited alongside, same era.
Word sense disambiguation with distribution estimation
Yee Seng Chan and Hwee Tou Ng
Cited in the paper.
When training and test sets are different: characterizing learning transfer
Amos Storkey
Cited in the paper.
Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers
Bianca Zadrozny and Charles Elkan
Cited in the paper.
Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan, and Zachary C Lipton · 2020
Closest in time.