{"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/measuring-the-quality-of-synthetic-data-for","title":"Measuring the quality of Synthetic data for use in competitions","arxiv_id":"1806.11345","date":"2018-06-29","proceeding":null,"authors":["James Jordon","Jinsung Yoon","Mihaela van der Schaar"],"abstract":"Machine learning has the potential to assist many communities in using the\nlarge datasets that are becoming more and more available. Unfortunately, much\nof that potential is not being realized because it would require sharing data\nin a way that compromises privacy. In order to overcome this hurdle, several\nmethods have been proposed that generate synthetic data while preserving the\nprivacy of the real data. In this paper we consider a key characteristic that\nsynthetic data should have in order to be useful for machine learning\nresearchers - the relative performance of two algorithms (trained and tested)\non the synthetic dataset should be the same as their relative performance (when\ntrained and tested) on the original dataset.","url_abs":"http://arxiv.org/abs/1806.11345v1","url_pdf":"http://arxiv.org/pdf/1806.11345v1.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":"measuring-the-quality-of-synthetic-data-for","repo_url":"https://github.com/jsyoon0823/SRA_TSTR","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1806.11345","atlas_url":"https://app.syntology.ai/?focus=1806.11345","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}