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Found 2 papers, 1 papers with code
Adapting to Label Shift with Bias-Corrected Calibration
Label shift refers to the phenomenon where the marginal probability p(y) of observing a particular class changes between the training and test distributions, while the conditional probability p(x|y) stays fixed.
A General Framework for Abstention Under Label Shift
In this work, we present a general framework for abstention that can be applied to optimize any metric of interest, that is adaptable to label shift at test time, and that works out-of-the-box with any classifier that can be calibrated.
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