English

On Classification-Calibration of Gamma-Phi Losses

Machine Learning 2023-12-13 v2 Machine Learning

Abstract

Gamma-Phi losses constitute a family of multiclass classification loss functions that generalize the logistic and other common losses, and have found application in the boosting literature. We establish the first general sufficient condition for the classification-calibration (CC) of such losses. To our knowledge, this sufficient condition gives the first family of nonconvex multiclass surrogate losses for which CC has been fully justified. In addition, we show that a previously proposed sufficient condition is in fact not sufficient. This contribution highlights a technical issue that is important in the study of multiclass CC but has been neglected in prior work.

Cite

@article{arxiv.2302.07321,
  title  = {On Classification-Calibration of Gamma-Phi Losses},
  author = {Yutong Wang and Clayton D. Scott},
  journal= {arXiv preprint arXiv:2302.07321},
  year   = {2023}
}

Comments

Appeared in COLT 2023

R2 v1 2026-06-28T08:40:14.181Z