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Prediction models can perform poorly when deployed to target distributions different from the training distribution.
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The iwildcam 2020 competition dataset
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Ethical machine learning in health care
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Assessing external validity over worst-case subpopulations
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Wilds: A benchmark of in-the-wild distribution shifts
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Measuring robustness to natural distribution shifts in image classification
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Learning models with uniform performance via distributionally robust optimization
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Mind the gap: Assessing temporal generalization in neural language models
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The risks of invariant risk minimization
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Extending the wilds benchmark for unsupervised adaptation
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From data to decisions: Distributionally robust optimization is optimal
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External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients
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Distributionally robust losses for latent covariate mixtures
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Elements of external validity: Framework, design, and analysis
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Propensity score models are better when post-calibrated
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Semiparametric doubly robust targeted double machine learning: a review
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