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, if not all, losses used in practice.
@article{arxiv.2304.03337,
title = {On the Learnability of Multilabel Ranking},
author = {Vinod Raman and Unique Subedi and Ambuj Tewari},
journal= {arXiv preprint arXiv:2304.03337},
year = {2023}
}