{"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/comparing-bileveljump-jl-formulations-support","title":"Comparing BilevelJuMP.jl Formulations: Support Vector Regression Hyperparameter Tuning","arxiv_id":"2305.17320","date":"2023-05-27","proceeding":null,"authors":["Joaquim Dias Garcia","Guilherme Bodin","Alexandre Street"],"abstract":"In this technical report, we compare multiple reformulation techniques and solvers that can be used with the Julia package BilevelJuMP. We focus on the special case of Hyperparameter Tuning for Support Vector Regression. We describe a bilevel model for the problem in question. Then we present code for generating data and models that solve the problem. Finally, we present results and a brief analysis.","url_abs":"https://arxiv.org/abs/2305.17320v1","url_pdf":"https://arxiv.org/pdf/2305.17320v1.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":"comparing-bileveljump-jl-formulations-support","repo_url":"https://github.com/joaquimg/bileveljumpbenchmarksvrht","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}