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Conditional independence and graphical models for rankings — arXiv:2611.04586

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.04586
License (as archived): CC0-1.0
Archived: 2026-11-04T18:55:57.612Z
SHA-256 of the archived text: 2a82412abec9f5c5f13ee24a43fd2cd4053ee3503b47acfdcd78bae282deb781
Stable URL of this page: https://metraipolis.com/documento/dc_beda18ff8cb5

How to cite

Conditional independence and graphical models for rankings — arXiv:2611.04586 — arXiv (en), https://arxiv.org/abs/2611.04586 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_beda18ff8cb5 (Metraipolis archive dc_beda18ff8cb5, sha256 2a82412abec9f5c5f13ee24a43fd2cd4053ee3503b47acfdcd78bae282deb781)

BibTeX

@misc{dcbeda18ff8cb5,
  title = {Conditional independence and graphical models for rankings — arXiv:2611.04586},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.04586},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_beda18ff8cb5; sha256 2a82412abec9f5c5f13ee24a43fd2cd4053ee3503b47acfdcd78bae282deb781},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.04586. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Ranking models define probability distributions over rankings and are fundamental in applications such as preference learning and decision-making. Without structural assumptions, these models are intractable due to their factorial complexity. Although independence assumptions are key to reducing complexity and enabling interpretability, standard notions of conditional independence must account for the mutual exclusivity constraints inherent to rankings. We introduce relative rank models, a probabilistic framework based on structured coarsenings of the ranking space that relax these constraints while preserving relative order information. This representation enables a well-defined notion of conditional independence for rankings and allows rankings to be modeled using Bayesian networks. As a result, standard methods for learning and probabilistic inference can be applied to rankings, with interpretable representations based on conditional independence. We further show that two prominent ranking models based on independence assumptions tailored to rankings arise as special cases, providing a unifying framework for independence in ranking models.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (stat.ML) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2611.04586 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-04T18:35:37.536Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).