# Distribution-free uncertainty quantification for classification under label shift

@inproceedings{Podkopaev2021DistributionfreeUQ, title={Distribution-free uncertainty quantification for classification under label shift}, author={Aleksandr Podkopaev and Aaditya Ramdas}, booktitle={UAI}, year={2021} }

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…

## 4 Citations

PAC Prediction Sets Under Covariate Shift

- Computer Science, MathematicsArXiv
- 2021

This work proposes a novel approach that addresses this challenge of rigorously quantify the uncertainty of model predictions by constructing probably approximately correct (PAC) prediction sets in the presence of covariate shift.

Top-label calibration

- Computer ScienceArXiv
- 2021

A histogram binning algorithm is formalized that reduces top-label multiclass calibration to the binary case, it is proved that it has clean theoretical guarantees without distributional assumptions, and a methodical study of its practical performance is performed.

Top-label calibration and multiclass-to-binary reductions

- Computer Science, Mathematics
- 2021

A new and arguably natural notion of top-label calibration is proposed, which requires the reported probability of the most likely label to be calibrated, using underlying binary calibration routines.

Tracking the risk of a deployed model and detecting harmful distribution shifts

- Mathematics, Computer ScienceArXiv
- 2021

This work designs simple sequential tools for testing if the difference between source (training) and target (test) distributions leads to a significant increase in a risk function of interest, like accuracy or calibration, and demonstrates the efficacy of the proposed framework through an extensive empirical study on a collection of simulated and real datasets.

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