On the Learnability of Multilabel Ranking
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 13 of its 50 references that are in the map. First posted on arXiv 6 Apr 2023.
Builds on
13 of 50 references.
References · 13
- 2022A Characterization of Multiclass Learnabilitycited by 66
- 2014Optimal Learners for Multiclass Problemscited by 65
- 2018Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classificationcited by 57
- 2013Deep Convolutional Ranking for Multilabel Image Annotationcited by 47
- 2015Exploit Bounding Box Annotations for Multi-label Object Recognitioncited by 39
- 2021Realizable Learning is All You Needcited by 25
- 2023Multiclass Online Learning and Uniform Convergencecited by 19
- 2018A Structured Prediction Approach for Label Rankingcited by 18
- 2020Closure Properties for Private Classification and Online Predictioncited by 15
- 2017Online Boosting Algorithms for Multi-label Rankingcited by 8
- 2016Generalization error bounds for learning to rank: Does the length of document lists matter?cited by 4
- 2023A Characterization of Multioutput Learnabilitycited by 2
1 more
- 2014Online Ranking with Top-1 Feedbackcited by 1
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Citing papers per year: 2023: 1; 2024: 0; 2025: 1.
2 papers.