Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization

AISTATS, pp. 3574-3582, 2021.

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Abstract:

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. I...More

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