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Learning invariant representations is an important requirement when training machine learning models that are driven by spurious correlations in the datasets.
Inference and missing data
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Daniel Levy, Yair Carmon, John C Duchi, and Aaron Sidford · 2020
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Hidden stratification causes clinically meaningful failures in machine learning for medical imaging
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An investigation of why overparameterization exacerbates spurious correlations
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Daniel Moyer, Shuyang Gao, Rob Brekelmans, Aram Galstyan, and Greg Ver Steeg · 2018
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