{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-permutation-symmetries-with-gips-in","title":"Learning permutation symmetries with gips in R","arxiv_id":"2307.00790","date":"2023-07-03","proceeding":null,"authors":["Adam Chojecki","Paweł Morgen","Bartosz Kołodziejek"],"abstract":"The study of hidden structures in data presents challenges in modern statistics and machine learning. We introduce the $\\mathbf{gips}$ package in R, which identifies permutation subgroup symmetries in Gaussian vectors. $\\mathbf{gips}$ serves two main purposes: exploratory analysis in discovering hidden permutation symmetries and estimating the covariance matrix under permutation symmetry. It is competitive to canonical methods in dimensionality reduction while providing a new interpretation of the results. $\\mathbf{gips}$ implements a novel Bayesian model selection procedure within Gaussian vectors invariant under the permutation subgroup introduced in Graczyk, Ishi, Ko{\\l}odziejek, Massam, Annals of Statistics, 50 (3) (2022).","url_abs":"https://arxiv.org/abs/2307.00790v3","url_pdf":"https://arxiv.org/pdf/2307.00790v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"learning-permutation-symmetries-with-gips-in","repo_url":"https://github.com/przechoj/gips_replication_code","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}