Modular Conformal Calibration

Charles Marx; Shengjia Zhao; Willie Neiswanger; Stefano Ermon

ICML 2022spotlight18 citations

Modular Conformal Calibration

Charles Marx, Shengjia Zhao, Willie Neiswanger, Stefano Ermon

Abstract

Uncertainty estimates must be calibrated (i.e., accurate) and sharp (i.e., informative) in order to be useful. This has motivated a variety of methods for

BibTeX

@InProceedings{pmlr-v162-marx22a,
  title = 	 {Modular Conformal Calibration},
  author =       {Marx, Charles and Zhao, Shengjia and Neiswanger, Willie and Ermon, Stefano},
  booktitle = 	 {Proceedings of the 39th International Conference on Machine Learning},
  pages = 	 {15180--15195},
  year = 	 {2022},
  editor = 	 {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
  volume = 	 {162},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {17--23 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v162/marx22a/marx22a.pdf},
  url = 	 {https://proceedings.mlr.press/v162/marx22a.html},
  abstract = 	 {Uncertainty estimates must be calibrated (i.e., accurate) and sharp (i.e., informative) in order to be useful. This has motivated a variety of methods for