On the Learnability of Multilabel Ranking
Vinod Raman, Unique Subedi, Ambuj Tewari
4/6/2023
cs.LGstat.ML
No confident code implementation found for this paper.
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, if not all, losses used in practice.
