Corpus ID: 232134824

Distribution-free uncertainty quantification for classification under label shift

@article{Podkopaev2021DistributionfreeUQ,
  title={Distribution-free uncertainty quantification for classification under label shift},
  author={Aleksandr Podkopaev and Aaditya Ramdas},
  journal={ArXiv},
  year={2021},
  volume={abs/2103.03323}
}
Trustworthy deployment of ML models requires a proper measure of uncertainty, especially in safety-critical applications. We focus on uncertainty quantification (UQ) for classification problems via two avenues — prediction sets using conformal prediction and calibration of probabilistic predictors by post-hoc binning — since these possess distribution-free guarantees for i.i.d. data. Two common ways of generalizing beyond the i.i.d. setting include handling covariate and label shift. Within the… Expand

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