UAI 2021poster109 citations
Distribution-free uncertainty quantification for classification under label shift
Aleksandr Podkopaev, Aaditya Ramdas
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
BibTeX
@InProceedings{pmlr-v161-podkopaev21a,
title = {Distribution-free uncertainty quantification for classification under label shift},
author = {Podkopaev, Aleksandr and Ramdas, Aaditya},
booktitle = {Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence},
pages = {844--853},
year = {2021},
editor = {de Campos, Cassio and Maathuis, Marloes H.},
volume = {161},
series = {Proceedings of Machine Learning Research},
month = {27--30 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v161/podkopaev21a/podkopaev21a.pdf},
url = {https://proceedings.mlr.press/v161/podkopaev21a.html},
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