Spectral Ranking with Covariates
Abstract
We consider approaches to the classical problem of establishing a statistical ranking on a given set of items from incomplete and noisy pairwise comparisons, and propose spectral algorithms able to leverage available covariate information about the items. We give a comprehensive study of several ways such side information can be useful in spectral ranking. We establish connections of the resulting algorithms to reproducing kernel Hilbert spaces and associated dependence measures, along with an extension to fair ranking using statistical parity. We present an extensive set of numerical experiments showcasing the competitiveness of the proposed algorithms with state-of-the-art methods.
Cite
Text
Chau et al. "Spectral Ranking with Covariates." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022. doi:10.1007/978-3-031-26419-1_5Markdown
[Chau et al. "Spectral Ranking with Covariates." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022.](https://mlanthology.org/ecmlpkdd/2022/chau2022ecmlpkdd-spectral/) doi:10.1007/978-3-031-26419-1_5BibTeX
@inproceedings{chau2022ecmlpkdd-spectral,
title = {{Spectral Ranking with Covariates}},
author = {Chau, Siu Lun and Cucuringu, Mihai and Sejdinovic, Dino},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2022},
pages = {70-86},
doi = {10.1007/978-3-031-26419-1_5},
url = {https://mlanthology.org/ecmlpkdd/2022/chau2022ecmlpkdd-spectral/}
}