# A Structured Prediction Approach for Label Ranking

arXiv: https://arxiv.org/abs/1807.02374 · HTML: https://mlsanity.com/paper/1807.02374/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2018.

## Built on by (most cited first, 18 of 18)

- [Predictive Inference with Weak Supervision](https://mlsanity.com/paper/2201.08315/) (2022)
- [Lifting Weak Supervision To Structured Prediction](https://mlsanity.com/paper/2211.13375/) (2022)
- [Optimizing Partial Area Under the Top-k Curve: Theory and Practice](https://mlsanity.com/paper/2209.01398/) (2022)
- [Vector-Valued Least-Squares Regression under Output Regularity Assumptions](https://mlsanity.com/paper/2211.08958/) (2022)
- [Geometry-Aware Adaptation for Pretrained Models](https://mlsanity.com/paper/2307.12226/) (2023)
- [Disambiguation of weak supervision with exponential convergence rates](https://mlsanity.com/paper/2102.02789/) (2021)
- [Multi-label Ranking: Mining Multi-label and Label Ranking Data](https://mlsanity.com/paper/2101.00583/) (2021)
- [Label Ranking through Nonparametric Regression](https://mlsanity.com/paper/2111.02749/) (2021)
- [Kernel-Based Generalized Median Computation for Consensus Learning](https://mlsanity.com/paper/2209.10208/) (2022)
- [On the Learnability of Multilabel Ranking](https://mlsanity.com/paper/2304.03337/) (2023)
- [Order-Based Pre-training Strategies for Procedural Text Understanding](https://mlsanity.com/paper/2404.04676/) (2024)
- [Deep Sketched Output Kernel Regression for Structured Prediction](https://mlsanity.com/paper/2406.09253/) (2024)
- [Multimodal Label Relevance Ranking via Reinforcement Learning](https://mlsanity.com/paper/2407.13221/) (2024)
- [From Weakly Supervised Learning to Active Learning](https://mlsanity.com/paper/2209.11629/) (2022)
- [A comparative analysis of rank aggregation methods for the partial label ranking problem](https://mlsanity.com/paper/2502.17077/) (2025)
- [Beyond Kemeny Medians: Consensus Ranking Distributions Definition, Properties and Statistical Learning](https://mlsanity.com/paper/2602.10640/) (2026)
- [Calibrated Preference Learning: The Case of Label Ranking](https://mlsanity.com/paper/2605.30447/) (2026)
- [Structure-aware Relative Policy Optimization for Ranking](https://mlsanity.com/paper/2607.25268/) (2026)

## 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.