A Unified View of Label Shift Estimation

NIPS 2020, 2020.

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We argue that these methods all employ calibration, either explicitly or implicitly, di ering only in the choice of calibration method and their optimization objective

Abstract:

Label shift describes the setting where although the label distribution might change between the source and target domains, the class-conditional probabilities (of data given a label) do not. There are two dominant approaches for estimating the label marginal. BBSE, a moment-matching approach based on confusion matrices, is provably con...More

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