Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization

• URL: http://arxiv.org/abs/2002.05660v1
• Date: Thu, 13 Feb 2020 17:37:53 GMT
• Title: Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization
• Authors: Vikas K. Garg, Adam Kalai, Katrina Ligett, and Zhiwei Steven Wu
• Abstract summary: 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.
• Score: 38.345670899258515
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. In our model, the training data given to a learning algorithm consists of multiple datasets each from a single domain drawn in turn from the meta-distribution. We study this model in three different problem settings---a multi-domain Massart noise setting, a decision tree multi-dataset setting, and a feature selection setting, and find that computationally efficient, polynomial-sample domain generalization is possible in each. Experiments demonstrate that our feature selection algorithm indeed ignores spurious correlations and improves generalization.

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