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TowardsaTheoreticalFrameworkof Out-of-DistributionGeneralization

Neural Information Processing Systems

Generalization to out-of-distribution (OOD) data is one of the central problems in modern machine learning. Recently, there is a surge of attempts to propose algorithms that mainly build upon the idea of extracting invariant features.