Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization

13 Feb 2020Vikas K. GargAdam KalaiKatrina LigettZhiwei Steven Wu

Domain generalization is the problem of machine learning when the training data and the test data come from different data domains. We present a simple theoretical model of learning to generalize across domains in which there is a meta-distribution over data distributions, and those data distributions may even have different supports... (read more)

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