# MORE-PLR: multi-output regression employed for partial label ranking

arXiv: https://arxiv.org/abs/2609.29386 · HTML: https://mlsanity.com/paper/2609.29386/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 41 references that are in the map.
- First posted on arXiv 24 Sep 2026.

## Builds on (most cited first, 6 of 6)

- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [A Survey of Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2312.14925/) (2023)
- [Deep Gaussian Processes: A Survey](https://mlsanity.com/paper/2106.12135/) (2021)
- [Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce Model](https://mlsanity.com/paper/2010.13118/) (2020)
- [Label Ranking through Nonparametric Regression](https://mlsanity.com/paper/2111.02749/) (2021)
- [A Multiclass Classification Approach to Label Ranking](https://mlsanity.com/paper/2002.09420/) (2020)

## Abstract (arXiv)

The partial label ranking problem is a supervised learning scenario that aims to fit a preference model that predicts a bucket order defined over a set of labels for a given input instance. This problem generalizes the well-known label ranking problem, which, in practice, is limited to outputting total orders of labels. Existing partial label ranking methods have primarily extended label ranking approaches to handle ties in predictions. This paper proposes using multi-output regression to address the partial label ranking problem, introducing an encoder that, during the learning phase, transforms the (possibly incomplete) rankings with ties of labels to multivariate regression targets, an underexplored perspective in both label ranking and partial label ranking. Moreover, during the inference phase, we introduce several post-hoc layers that convert the multi-output regression results into the output bucket order to effectively implement this approach. This framework provides learning strategies that are competitive with the current state-of-the-art partial label ranking methods, as demonstrated through experimental evaluations.