NeurIPS 2023spotlight0 citations
On the Learnability of Multilabel Ranking
Vinod Raman, UNIQUE SUBEDI, Ambuj Tewari
Abstract
Multilabel ranking is a central task in machine learning. However, the most fundamental question of learnability in a multilabel ranking setting with relevance-score feedback remains unanswered. In this work, we characterize the learnability of multilabel ranking problems in both batch and online settings for a large family of ranking losses. Along the way, we give two equivalence classes of ranking losses based on learnability that capture most losses used in practice.
Multilabel RankingPAC LearningOnline Learning
BibTeX
@inproceedings{
raman2023on,
title={On the Learnability of Multilabel Ranking},
author={Vinod Raman and UNIQUE SUBEDI and Ambuj Tewari},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=cwBeRBe9hq}
}