On the Learnability of Multilabel Ranking
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.
Cite
Text
Raman et al. "On the Learnability of Multilabel Ranking." Neural Information Processing Systems, 2023.Markdown
[Raman et al. "On the Learnability of Multilabel Ranking." Neural Information Processing Systems, 2023.](https://mlanthology.org/neurips/2023/raman2023neurips-learnability/)BibTeX
@inproceedings{raman2023neurips-learnability,
title = {{On the Learnability of Multilabel Ranking}},
author = {Raman, Vinod and Subedi, Unique and Tewari, Ambuj},
booktitle = {Neural Information Processing Systems},
year = {2023},
url = {https://mlanthology.org/neurips/2023/raman2023neurips-learnability/}
}