Identifying unpredictable test examples with worst-case guarantees

ITA, pp. 1-14, 2020.

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Abstract:

Often times, whether it be for adversarial or natural reasons, the distributions of test and training data differ. We give an algorithm that, given sets of training and test examples, identifies regions of test examples that cannot be predicted with low error. These regions are classified as ƒ or equivalently omitted from classification. ...More

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