Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization

Neural Information Processing Systems 

Much remains to be understood, however, in statistical learning under distribution shifts. This paper focuses on a distribution shift setting where train and test distributions can be related by classes of (data) transformation maps.

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