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We develop a methodology for assessing the robustness of models to subpopulation shift---specifically, their ability to generalize to novel data subpopulations that were not observed during training.
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“Unsupervised domain adaptation by backpropagation”
Yaroslav Ganin and Victor Lempitsky · 2015
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“Data-driven distributionally robust optimization using the Wasserstein metric: Performance guarantees and tractable reformulations”
Peyman Esfahani and Daniel Kuhn · 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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Weihua Hu, Gang Niu, Issei Sato and Masashi Sugiyama · 2018
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“Towards deep learning models resistant to adversarial attacks”
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