Detecting and Correcting for Label Shift with Black Box PredictorsEI

    Cited by: 34|Bibtex|15|

    ICML, pp. 3122-3130, 2018.

    Abstract:

    Faced with distribution shift between training and test set, we wish to detect and quantify the shift, and to correct our classifiers without test set labels. Motivated by medical diagnosis, where diseases (targets), cause symptoms (observations), we focus on label shift, where the label marginal $p(y)$ changes but the conditional $p(x|y)...More
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