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A Structured Prediction Approach for Label Ranking

arXiv 1807.02374 ↗First posted 6 Jul 2018stat.ML cs.LGcited by 18

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Abstract (arXiv)

We propose to solve a label ranking problem as a structured output regression task. We adopt a least square surrogate loss approach that solves a supervised learning problem in two steps: the regression step in a well-chosen feature space and the pre-image step. We use specific feature maps/embeddings for ranking data, which convert any ranking/permutation into a vector representation. These embeddings are all well-tailored for our approach, either by resulting in consistent estimators, or by solving trivially the pre-image problem which is often the bottleneck in structured prediction. We also propose their natural extension to the case of partial rankings and prove their efficiency on real-world datasets.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jul 2018.

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Citing papers per year: 2021: 3; 2022: 6; 2023: 2; 2024: 3; 2025: 1; 2026: 3.

18 papers.

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  1. 2022Predictive Inference with Weak Supervisioncited by 13
  2. 2022Lifting Weak Supervision To Structured Predictioncited by 10
  3. 2022Optimizing Partial Area Under the Top-k Curve: Theory and Practicecited by 9
  4. 2022Vector-Valued Least-Squares Regression under Output Regularity Assumptionscited by 5
  5. 2023Geometry-Aware Adaptation for Pretrained Modelscited by 5
  6. 2021Disambiguation of weak supervision with exponential convergence ratescited by 4
  7. 2021Multi-label Ranking: Mining Multi-label and Label Ranking Datacited by 3
  8. 2021Label Ranking through Nonparametric Regressioncited by 3
  9. 2022Kernel-Based Generalized Median Computation for Consensus Learningcited by 3
  10. 2023On the Learnability of Multilabel Rankingcited by 2
  11. 2024Order-Based Pre-training Strategies for Procedural Text Understandingcited by 2
  12. 2024Deep Sketched Output Kernel Regression for Structured Predictioncited by 2
6 more
  1. 2024Multimodal Label Relevance Ranking via Reinforcement Learningcited by 1
  2. 2022From Weakly Supervised Learning to Active Learningcited by 0
  3. 2025A comparative analysis of rank aggregation methods for the partial label ranking problemcited by 0
  4. 2026Beyond Kemeny Medians: Consensus Ranking Distributions Definition, Properties and Statistical Learningcited by 0
  5. 2026Calibrated Preference Learning: The Case of Label Rankingcited by 0
  6. 2026Structure-aware Relative Policy Optimization for Rankingcited by 0