{"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/volcanoml-speeding-up-end-to-end-automl-via","title":"VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition","arxiv_id":"2107.08861","date":"2021-07-19","proceeding":null,"authors":["Yang Li","Yu Shen","Wentao Zhang","Jiawei Jiang","Bolin Ding","Yaliang Li","Jingren Zhou","Zhi Yang","Wentao Wu","Ce Zhang","Bin Cui"],"abstract":"End-to-end AutoML has attracted intensive interests from both academia and industry, which automatically searches for ML pipelines in a space induced by feature engineering, algorithm/model selection, and hyper-parameter tuning. Existing AutoML systems, however, suffer from scalability issues when applying to application domains with large, high-dimensional search spaces. We present VolcanoML, a scalable and extensible framework that facilitates systematic exploration of large AutoML search spaces. VolcanoML introduces and implements basic building blocks that decompose a large search space into smaller ones, and allows users to utilize these building blocks to compose an execution plan for the AutoML problem at hand. VolcanoML further supports a Volcano-style execution model - akin to the one supported by modern database systems - to execute the plan constructed. 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