{"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/transfer-learning-for-performance-modeling-of","title":"Transfer Learning for Performance Modeling of Configurable Systems: A Causal Analysis","arxiv_id":"1902.10119","date":"2019-02-26","proceeding":null,"authors":["Mohammad Ali Javidian","Pooyan Jamshidi","Marco Valtorta"],"abstract":"Modern systems (e.g., deep neural networks, big data analytics, and\ncompilers) are highly configurable, which means they expose different\nperformance behavior under different configurations. The fundamental challenge\nis that one cannot simply measure all configurations due to the sheer size of\nthe configuration space. Transfer learning has been used to reduce the\nmeasurement efforts by transferring knowledge about performance behavior of\nsystems across environments. Previously, research has shown that statistical\nmodels are indeed transferable across environments. In this work, we\ninvestigate identifiability and transportability of causal effects and\nstatistical relations in highly-configurable systems. Our causal analysis\nagrees with previous exploratory analysis \\cite{Jamshidi17} and confirms that\nthe causal effects of configuration options can be carried over across\nenvironments with high confidence. We expect that the ability to carry over\ncausal relations will enable effective performance analysis of\nhighly-configurable systems.","url_abs":"http://arxiv.org/abs/1902.10119v1","url_pdf":"http://arxiv.org/pdf/1902.10119v1.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":"transfer-learning-for-performance-modeling-of","repo_url":"https://github.com/majavid/AAAI-WHY-2019","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}