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Empirical risk minimization (ERM) is known in practice to be non-robust to distributional shift where the training and the test distributions are different.
The logit model and response-based samples
Yu Xie and Charles F Manski · 1989
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Improving predictive inference under covariate shift by weighting the log-likelihood function
Hidetoshi Shimodaira · 2000
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Justice as fairness: A restatement
John Rawls · 2001
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Introduction to the non-asymptotic analysis of random matrices
Roman Vershynin · 2010
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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Learning fair representations
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
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Tagging performance correlates with author age
Dirk Hovy and Anders Søgaard · 2015
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Simultaneous deep transfer across domains and tasks
Eric Tzeng, Judy Hoffman, Trevor Darrell, and Kate Saenko · 2015
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Demographic dialectal variation in social media: A case study of African-American English
Su Lin Blodgett, Lisa Green, and Brendan O’Connor · 2016
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
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Unsupervised domain adaptation with residual transfer networks
Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I Jordan · 2016
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Deep coral: Correlation alignment for deep domain adaptation, 2016
Baochen Sun and Kate Saenko · 2016
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Counterfactual fairness
Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva · 2017
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Unified deep supervised domain adaptation and generalization
Saeid Motiian, Marco Piccirilli, Donald A Adjeroh, and Gianfranco Doretto · 2017
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Gender and dialect bias in youtube’s automatic captions
Rachael Tatman · 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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Learning models with uniform performance via distributionally robust optimization
John Duchi and Hongseok Namkoong · 2018
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Characterizing implicit bias in terms of optimization geometry
Suriya Gunasekar, Jason Lee, Daniel Soudry, and Nathan Srebro · 2018
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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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Rethinking importance weighting for deep learning under distribution shift
Tongtong Fang, Nan Lu, Gang Niu, and Masashi Sugiyama · 2020
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Gradient descent follows the regularization path for general losses
Ziwei Ji, Miroslav Dudík, Robert E. Schapire, and Matus Telgarsky · 2020
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Adjusting decision boundary for class imbalanced learning
Byungju Kim and Junmo Kim · 2020
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Identifying and compensating for feature deviation in imbalanced deep learning
Han-Jia Ye, Hong-You Chen, De-Chuan Zhan, and Wei-Lun Chao · 2020
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In search of lost domain generalization
Ishaan Gulrajani and David Lopez-Paz · 2021
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Label-imbalanced and group-sensitive classification under overparameterization
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Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clement Hongler · 2018
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Domain generalization with adversarial feature learning
Haoliang Li, Sinno Jialin Pan, Shiqi Wang, and Alex C Kot · 2018
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The implicit bias of gradient descent on separable data
Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Suriya Gunasekar, and Nathan Srebro · 2018
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A convergence theory for deep learning via over-parameterization
Zeyuan Allen-Zhu, Yuanzhi Li, and Zhao Song · 2019
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 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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Learning imbalanced datasets with label-distribution-aware margin loss
Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, and Tengyu Ma · 2019
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Ganesh Ramachandra Kini, Orestis Paraskevas, Samet Oymak, and Christos Thrampoulidis · 2021
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Wilds: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, Etienne David, Ian Stavness, Wei Guo, Berton Earnshaw, Imran Haque, Sara M Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, and Percy Liang · 2021
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Just train twice: Improving group robustness without training group information
Evan Z Liu, Behzad Haghgoo, Annie S Chen, Aditi Raghunathan, Pang Wei Koh, Shiori Sagawa, Percy Liang, and Chelsea Finn · 2021
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Long-tail learning via logit adjustment
Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, and Sanjiv Kumar · 2021
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The risks of invariant risk minimization
Elan Rosenfeld, Pradeep Kumar Ravikumar, and Andrej Risteski · 2021
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Contrastive learning based hybrid networks for long-tailed image classification
Peng Wang, Kai Han, Xiu-Shen Wei, Lei Zhang, and Lei Wang · 2021
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Understanding the role of importance weighting for deep learning
Da Xu, Yuting Ye, and Chuanwei Ruan · 2021
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Self-supervised learning is more robust to dataset imbalance
Hong Liu, Jeff Z. HaoChen, Adrien Gaidon, and Tengyu Ma · 2022
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Extending the WILDS benchmark for unsupervised adaptation
Shiori Sagawa, Pang Wei Koh, Tony Lee, Irena Gao, Sang Michael Xie, Kendrick Shen, Ananya Kumar, Weihua Hu, Michihiro Yasunaga, Henrik Marklund, Sara Beery, Etienne David, Ian Stavness, Wei Guo, Jure Leskovec, Kate Saenko, Tatsunori Hashimoto, Sergey Levine, Chelsea Finn, and Percy Liang · 2022
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Is importance weighting incompatible with interpolating classifiers?
Ke Alexander Wang, Niladri Shekhar Chatterji, Saminul Haque, and Tatsunori Hashimoto · 2022
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A fine-grained analysis on distribution shift
Olivia Wiles, Sven Gowal, Florian Stimberg, Sylvestre-Alvise Rebuffi, Ira Ktena, Krishnamurthy Dj Dvijotham, and Ali Taylan Cemgil · 2022
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