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Human-generated pipelines (HI-pipelines) typically use a wide range of any operations or libraries but are highly experience- and heuristic-based. In contrast, machine-generated pipelines (AI-pipelines), a.k.a. AutoML, often adopt a predefined set of sophisticated operations and are search-based and optimized. These two common practices are mutually complementary. In this paper, we study a new problem that, given an HI-pipeline and an AI-pipeline for the same ML task, can we combine them to get a new pipeline (HAI-pipeline) that is better than the provided HI-pipeline and AI-pipeline? We propose HAIPipe, a framework to address the problem, which adopts an enumeration-sampling strategy to carefully select the best performing combined pipeline. We also introduce a reinforcement learning (RL) based approach to search an optimized AI-pipeline. Extensive experiments using 1400+ real-world HI-pipelines (Jupyter notebooks from Kaggle) verify that HAIPipe can significantly outperform the approaches using either HI-pipelines or AI-pipelines alone.<\/jats:p>","DOI":"10.1145\/3588945","type":"journal-article","created":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T13:42:05Z","timestamp":1685454125000},"page":"1-26","source":"Crossref","is-referenced-by-count":25,"title":["HAIPipe: Combining Human-generated and Machine-generated Pipelines for Data Preparation"],"prefix":"10.1145","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-5331-5829","authenticated-orcid":false,"given":"Sibei","family":"Chen","sequence":"first","affiliation":[{"name":"Renmin University of China, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2832-0295","authenticated-orcid":false,"given":"Nan","family":"Tang","sequence":"additional","affiliation":[{"name":"QCRI, Doha, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4729-9903","authenticated-orcid":false,"given":"Ju","family":"Fan","sequence":"additional","affiliation":[{"name":"Renmin University of China, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7018-0640","authenticated-orcid":false,"given":"Xuemi","family":"Yan","sequence":"additional","affiliation":[{"name":"Renmin University of China, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8080-5594","authenticated-orcid":false,"given":"Chengliang","family":"Chai","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1398-0621","authenticated-orcid":false,"given":"Guoliang","family":"Li","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5757-9135","authenticated-orcid":false,"given":"Xiaoyong","family":"Du","sequence":"additional","affiliation":[{"name":"Renmin University of China, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,5,30]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/2503308.2188395"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313602"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/978--3-030--34770--3_10"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3209889.3209891"},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2013.104"},{"key":"e_1_2_2_6_1","unstructured":"Copilot. 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