{"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/flash-fast-bayesian-optimization-for-data","title":"FLASH: Fast Bayesian Optimization for Data Analytic Pipelines","arxiv_id":"1602.06468","date":"2016-02-20","proceeding":null,"authors":["Yuyu Zhang","Mohammad Taha Bahadori","Hang Su","Jimeng Sun"],"abstract":"Modern data science relies on data analytic pipelines to organize\ninterdependent computational steps. Such analytic pipelines often involve\ndifferent algorithms across multiple steps, each with its own hyperparameters.\nTo achieve the best performance, it is often critical to select optimal\nalgorithms and to set appropriate hyperparameters, which requires large\ncomputational efforts. Bayesian optimization provides a principled way for\nsearching optimal hyperparameters for a single algorithm. However, many\nchallenges remain in solving pipeline optimization problems with\nhigh-dimensional and highly conditional search space. In this work, we propose\nFast LineAr SearcH (FLASH), an efficient method for tuning analytic pipelines.\nFLASH is a two-layer Bayesian optimization framework, which firstly uses a\nparametric model to select promising algorithms, then computes a nonparametric\nmodel to fine-tune hyperparameters of the promising algorithms. FLASH also\nincludes an effective caching algorithm which can further accelerate the search\nprocess. Extensive experiments on a number of benchmark datasets have\ndemonstrated that FLASH significantly outperforms previous state-of-the-art\nmethods in both search speed and accuracy. Using 50% of the time budget, FLASH\nachieves up to 20% improvement on test error rate compared to the baselines.\nFLASH also yields state-of-the-art performance on a real-world application for\nhealthcare predictive modeling.","url_abs":"http://arxiv.org/abs/1602.06468v3","url_pdf":"http://arxiv.org/pdf/1602.06468v3.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":"flash-fast-bayesian-optimization-for-data","repo_url":"https://github.com/yuyuz/FLASH","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}