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Spotlight Poster

On the Learnability of Multilabel Ranking

Vinod Raman · UNIQUE SUBEDI · Ambuj Tewari

Great Hall & Hall B1+B2 (level 1) #1819
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Wed 13 Dec 3 p.m. PST — 5 p.m. PST


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.

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