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Machine learning models often encounter distribution shifts when deployed in the real world.
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Arthur Gretton, Alex Smola, Jiayuan Huang, Marcel Schmittfull, Karsten Borgwardt, and Bernhard Schölkopf · 2009
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Domain adaptation: Learning bounds and algorithms
Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh · 2009
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Dataset shift in machine learning
Joaquin Quiñonero-Candela, Masashi Sugiyama, Neil D Lawrence, and Anton Schwaighofer · 2009
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Online domain adaptation of a pre-trained cascade of classifiers
Vidit Jain and Erik Learned-Miller · 2011
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Online learning and online convex optimization
Shai Shalev-Shwartz et al · 2011
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Kevin P Murphy · 2012
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On causal and anticausal learning
Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, and Joris M Mooij · 2012
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Deepjdot: Deep joint distribution optimal transport for unsupervised domain adaptation
Bharath Bhushan Damodaran, Benjamin Kellenberger, Rémi Flamary, Devis Tuia, and Nicolas Courty · 2018
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Algorithms and theory for multiple-source adaptation
Judy Hoffman, Mehryar Mohri, and Ningshan Zhang · 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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Wasserstein distance guided representation learning for domain adaptation
Jian Shen, Yanru Qu, Weinan Zhang, and Yong Yu · 2018
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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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Learning imbalanced datasets with label-distribution-aware margin loss
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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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Continuous manifold based adaptation for evolving visual domains
Judy Hoffman, Trevor Darrell, and Kate Saenko · 2014
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Concrete problems in ai safety
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Learning deep representation for imbalanced classification
Chen Huang, Yining Li, Chen Change Loy, and Xiaoou Tang · 2016
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Return of frustratingly easy domain adaptation
Baochen Sun, Jiashi Feng, and Kate Saenko · 2016
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Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, and Tengyu Ma · 2019
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Class-balanced loss based on effective number of samples
Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, and Serge Belongie · 2019
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Contrastive adaptation network for unsupervised domain adaptation
Guoliang Kang, Lu Jiang, Yi Yang, and Alexander G Hauptmann · 2019
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Online model distillation for efficient video inference
Ravi Teja Mullapudi, Steven Chen, Keyi Zhang, Deva Ramanan, and Kayvon Fatahalian · 2019
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Beyond sharing weights for deep domain adaptation
Artem Rozantsev, Mathieu Salzmann, and Pascal Fua · 2019
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Maximum likelihood with bias-corrected calibration is hard-to-beat at label shift adaptation
Amr Alexandari, Anshul Kundaje, and Avanti Shrikumar · 2020
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A unified view of label shift estimation
Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan, and Zachary C Lipton · 2020
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Test-time training with self-supervision for generalization under distribution shifts
Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei Efros, and Moritz Hardt · 2020
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Domain adaptation with conditional distribution matching and generalized label shift
Remi Tachet des Combes, Han Zhao, Yu-Xiang Wang, and Geoffrey J Gordon · 2020
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