{"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/a-structured-prediction-approach-for-label","title":"A Structured Prediction Approach for Label Ranking","arxiv_id":"1807.02374","date":"2018-07-06","proceeding":"NeurIPS 2018 12","authors":["Anna Korba","Alexandre Garcia","Florence d'Alché Buc"],"abstract":"We propose to solve a label ranking problem as a structured output regression\ntask. We adopt a least square surrogate loss approach that solves a supervised\nlearning problem in two steps: the regression step in a well-chosen feature\nspace and the pre-image step. We use specific feature maps/embeddings for\nranking data, which convert any ranking/permutation into a vector\nrepresentation. These embeddings are all well-tailored for our approach, either\nby resulting in consistent estimators, or by solving trivially the pre-image\nproblem which is often the bottleneck in structured prediction. We also propose\ntheir natural extension to the case of partial rankings and prove their\nefficiency on real-world datasets.","url_abs":"http://arxiv.org/abs/1807.02374v1","url_pdf":"http://arxiv.org/pdf/1807.02374v1.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":"abstracts"},"code_links":[{"paper_slug":"a-structured-prediction-approach-for-label","repo_url":"https://github.com/akorba/Structured_Approach_Label_Ranking","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"structured-prediction","task_name":"Structured Prediction"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.02374","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}