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Robustness is of central importance in machine learning and has given rise to the fields of domain generalization and invariant learning, which are concerned with improving performance on a test distribution distinct from but related to the training distribution.
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Nancy Cartwright · 2003
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Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Haji Mohammad Saleem, Kelly P Dillon, Susan Benesch, and Derek Ruths · 2017
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Joy Buolamwini and Timnit Gebru · 2018
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Christina Heinze-Deml, Jonas Peters, and Nicolai Meinshausen · 2018
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Svetlana Kiritchenko and Saif M. Mohammad · 2018
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Matt J. Kusner, Joshua R. Loftus, Chris Russell, and Ricardo Silva · 2018
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Ya Li, Mingming Gong, Xinmei Tian, Tongliang Liu, and Dacheng Tao · 2018
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Ameya Vaidya, Feng Mai, and Yue Ning · 2019
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Tim vor der Brück and Marc Pouly · 2019
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https://medium.com/the-false-positive/unintended-bias-and-names-of-frequently-targeted-groups-8e0b81f80a23
Unintended bias and names of frequently targeted groups · 2020
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Daniel Borkan, Lucas Dixon, Jeffrey Sorensen, Nithum Thain, and Lucy Vasserman · 2019
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An empirical study of invariant risk minimization
Yo Joong Choe, Jiyeon Ham, and Kyubyong Park · 2019
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Counterfactual fairness in text classification through robustness
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Nils Reimers and Iryna Gurevych · 2019
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