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Importance weighting is a classic technique to handle distribution shifts.
“Optimisation and stability theory for economic analysis”
Brian Beavis and Ian Dobbs · 1990
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“Improving predictive inference under covariate shift by weighting the log-likelihood function”
Hidetoshi Shimodaira · 2000
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“Impossibility of successful classification when useful features are rare and weak”
Jiashun Jin · 2009
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“Robust learning under uncertain test distributions: Relating covariate shift to model misspecification”
Junfeng Wen, Chun-Nam Yu and Russell Greiner · 2014
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“The implicit bias of gradient descent on separable data”
Daniel Soudry, Elad Hoffer, Mor Nacson, Suriya Gunasekar and Nathan Srebro · 2018
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“High-dimensional probability: An introduction with applications in data science”
Roman Vershynin · 2018
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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
Earlier work this paper cites.
“The implicit bias of gradient descent on nonseparable data”
Ziwei Ji and Matus Telgarsky · 2019
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“Gradient descent maximizes the margin of homogeneous neural networks”
Kaifeng Lyu and Jian Li · 2019
Cited alongside, same era.
“Convergence of gradient descent on separable data”
Mor Nacson, Jason Lee, Suriya Gunasekar, Pedro Savarese, Nathan Srebro and Daniel Soudry · 2019
Cited alongside, same era.
Shiori Sagawa, Pang Koh, Tatsunori Hashimoto and Percy Liang · 2019
Cited alongside, same era.
“Gradient descent follows the regularization path for general losses”
Ziwei Ji, Miroslav Dudik, Robert Schapire and Matus Telgarsky · 2020
Cited alongside, same era.
“Identifying and compensating for feature deviation in imbalanced deep learning”
Han-Jia Ye, Hong-You Chen, De-Chuan Zhan and Wei-Lun Chao · 2020
Later among the works it cites.
“Risk bounds for over-parameterized maximum margin classification on sub-Gaussian mixtures”
Yuan Cao, Quanquan Gu and Mikhail Belkin · 2021
Closest in time.
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Niladri Chatterji and Philip Long · 2021
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Ganesh Kini, Orestis Paraskevas, Samet Oymak and Christos Thrampoulidis · 2021
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Ziwei Ji and Matus Telgarsky · 2020
Cited alongside, same era.
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Aditya Menon, Sadeep Jayasumana, Ankit Rawat, Himanshu Jain, Andreas Veit and Sanjiv Kumar · 2020
Cited alongside, same era.
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Da Xu, Yuting Ye and Chuanwei Ruan · 2020
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