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The label shift problem refers to the supervised learning setting where the train and test label distributions do not match.
The logit model and response-based samples
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Adjusting the outputs of a classifier to new a priori probabilities: A simple procedure
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Minimax regret classifier for imprecise class distributions
Rocío Alaiz-Rodríguez, Alicia Guerrero-Curieses, and Jesús Cid-Sueiro · 2007
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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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Mixture regression for covariate shift
Amos J Storkey and Masashi Sugiyama · 2007
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Learning from imbalanced data
Haibo He and Edwardo A. Garcia · 2009
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Proximal splitting methods in signal processing
Patrick L Combettes and Jean-Christophe Pesquet · 2011
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Domain generalization via invariant feature representation
Krikamol Muandet, David Balduzzi, and Bernhard Schölkopf · 2013
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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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Convex optimization: Algorithms and complexity, 2014
Sébastien Bubeck · 2014
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Semi-supervised learning of class balance under class-prior change by distribution matching
Marthinus Christoffel du Plessis and Masashi Sugiyama · 2014
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Generative adversarial nets
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio · 2014
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Lectures on Stochastic Programming: Modeling and Theory, Second Edition
Alexander Shapiro, Darinka Dentcheva, and Andrzej Ruszczyński · 2014
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Stochastic gradient methods for distributionally robust optimization with f-divergences
H. Namkoong and J. Duchi · 2016
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Learning with average top-k loss
Yanbo Fan, Siwei Lyu, Yiming Ying, and Baogang Hu · 2017
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Variance-based regularization with convex objectives
Hongseok Namkoong and John C Duchi · 2017
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Certifying some distributional robustness with principled adversarial training
Aman Sinha, Hongseok Namkoong, and John Duchi · 2017
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Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P. Gummadi · 2017
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Empirical risk minimization under fairness constraints
Michele Donini, Luca Oneto, Shai Ben-David, John Shawe-Taylor, and Massimiliano Pontil · 2018
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Learning models with uniform performance via distributionally robust optimization
J. Duchi and H. Namkoong · 2018
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On gradient descent ascent for nonconvex-concave minimax problems, 2019
Tianyi Lin, Chi Jin, and Michael I. Jordan · 2019
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Agnostic federated learning
Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh · 2019
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Distributionally robust optimization: A review, 2019
Hamed Rahimian and Sanjay Mehrotra · 2019
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Efficient algorithms for smooth minimax optimization, 2019
Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, and Sewoong Oh · 2019
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Fairness risk measures
Robert C. Williamson and Aditya Krishna Menon · 2019
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Domain adaptation with conditional distribution matching and generalized label shift
Remi Tachet des Combes, Han Zhao, Yu-Xiang Wang, and Geoff Gordon · 2020
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Fairness without demographics in repeated loss minimization
Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang · 2018
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Does distributionally robust supervised learning give robust classifiers?
Weihua Hu, Gang Niu, Issei Sato, and Masashi Sugiyama · 2018
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An introduction to domain adaptation and transfer learning
Wouter M. Kouw and Marco Loog · 2018
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Detecting and correcting for label shift with black box predictors
Zachary Lipton, Yu-Xiang Wang, and Alexander Smola · 2018
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Certifiable distributional robustness with principled adversarial training
Aman Sinha, Hongseok Namkoong, and John Duchi · 2018
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Invariant risk minimization, 2019
Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Regularized learning for domain adaptation under label shifts
Kamyar Azizzadenesheli, Anqi Liu, Fanny Yang, and Animashree Anandkumar · 2019
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Distributionally robust losses for latent covariate mixtures, 2020
John Duchi, Tatsunori Hashimoto, and Hongseok Namkoong · 2020
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Distributionally robust counterfactual risk minimization
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A unified view of label shift estimation, 2020
Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan, and Zachary C. Lipton · 2020
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Ltf: A label transformation framework for correcting label shift
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Decoupling representation and classifier for long-tailed recognition
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Large-scale methods for distributionally robust optimization
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Near-optimal algorithms for minimax optimization, 2020
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An online method for distributionally deep robust optimization, 2020
Qi Qi, Zhishuai Guo, Yi Xu, Rong Jin, and Tianbao Yang · 2020
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Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization
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Weakly-convex concave min-max optimization: Provable algorithms and applications in machine learning, 2021
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