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We give a method for proactively identifying small, plausible shifts in distribution which lead to large differences in model performance.
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Causality
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The clinician and dataset shift in artificial intelligence
S. G. Finlayson, A. Subbaswamy, K. Singh, J. Bowers, A. Kupke, J. Zittrain, I. S. Kohane, and S. Saria · 2021
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Conditional variance penalties and domain shift robustness
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Evaluating model performance under worst-case subpopulations
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Regularizing towards causal invariance: Linear models with proxies
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Anchor regression: Heterogeneous data meet causality
D. Rothenhäusler, N. Meinshausen, P. Bühlmann, and J. Peters · 2021
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Distributionally robust losses for latent covariate mixtures
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Diagnosing gender bias in image recognition systems
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SciPy 1.0: Fundamental algorithms for scientific computing in Python
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Evaluating model robustness and stability to dataset shift
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Counterfactual invariance to spurious correlations: Why and how to pass stress tests
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Leveraging unlabeled data to predict Out-of-Distribution performance
S. Garg, S. Balakrishnan, Z. C. Lipton, B. Neyshabur, and H. Sedghi · 2022
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Assessing generalization of SGD via disagreement
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Distributional anchor regression
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Out-of-distribution generalization in the presence of Nuisance-Induced spurious correlations
A. Puli, L. H. Zhang, E. K. Oermann, and R. Ranganath · 2022
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