Distribution-free uncertainty quantification for classification under
label shift
- URL: http://arxiv.org/abs/2103.03323v1
- Date: Thu, 4 Mar 2021 20:51:03 GMT
- Title: Distribution-free uncertainty quantification for classification under
label shift
- Authors: Aleksandr Podkopaev, Aaditya Ramdas
- Abstract summary: We focus on uncertainty quantification (UQ) for classification problems via two avenues.
We first argue that label shift hurts UQ, by showing degradation in coverage and calibration.
We examine these techniques theoretically in a distribution-free framework and demonstrate their excellent practical performance.
- Score: 105.27463615756733
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 context of distribution-free UQ,
the former has already received attention, but not the latter. It is known that
label shift hurts prediction, and we first argue that it also hurts UQ, by
showing degradation in coverage and calibration. Piggybacking on recent
progress in addressing label shift (for better prediction), we examine the
right way to achieve UQ by reweighting the aforementioned conformal and
calibration procedures whenever some unlabeled data from the target
distribution is available. We examine these techniques theoretically in a
distribution-free framework and demonstrate their excellent practical
performance.
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