Part of Advances in Neural Information Processing Systems 36 (NeurIPS 2023) Main Conference Track
Vinod Raman, UNIQUE SUBEDI, Ambuj Tewari
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.