{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T13:52:18Z","timestamp":1758981138769,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":10,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T00:00:00Z","timestamp":1660435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,8,14]]},"DOI":"10.1145\/3534678.3542636","type":"proceedings-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T19:06:41Z","timestamp":1660331201000},"page":"4828-4829","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Automated Machine Learning & Tuning with FLAML"],"prefix":"10.1145","author":[{"given":"Chi","family":"Wang","sequence":"first","affiliation":[{"name":"Microsoft Research, Redmond, WA, USA"}]},{"given":"Qingyun","family":"Wu","sequence":"additional","affiliation":[{"name":"The Pennsylvania State University, State College, PA, USA"}]},{"given":"Xueqing","family":"Liu","sequence":"additional","affiliation":[{"name":"Stevens Institute of Technology, Hoboken, NJ, USA"}]},{"given":"Luis","family":"Quintanilla","sequence":"additional","affiliation":[{"name":"Microsoft Corporation, Redmond, WA, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"2021. AutoML Market. ReportLinker (Nov 2021). https:\/\/www.reportlinker.com\/p06191010\/AutoML-Market.html?utm_source=GNW"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","unstructured":"Moe Kayali and Chi Wang. 2022. Mining Robust Default Configurations for Resource-constrained AutoML. https:\/\/doi.org\/10.48550\/ARXIV.2202.09927","DOI":"10.48550\/ARXIV.2202.09927"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3470827"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Xueqing Liu and Chi Wang. 2021. An Empirical Study on Hyperparameter Optimization for Fine-Tuning Pre-trained Language Models. In the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing. 2286--2300.","DOI":"10.18653\/v1\/2021.acl-long.178"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3406706"},{"key":"e_1_3_2_1_6_1","volume-title":"International Conference on Learning Representations.","author":"Wang Chi","year":"2020","unstructured":"Chi Wang, Qingyun Wu, Silu Huang, and Amin Saied. 2020. Economic hyperparameter optimization with blended search strategy. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_7_1","volume-title":"FLAML: A Fast and Lightweight AutoML Library. In MLSys.","author":"Wang Chi","year":"2021","unstructured":"Chi Wang, Qingyun Wu, Markus Weimer, and Erkang Zhu. 2021. FLAML: A Fast and Lightweight AutoML Library. In MLSys."},{"key":"e_1_3_2_1_8_1","volume-title":"arXiv preprint arXiv:2111.06495","author":"Wu Qingyun","year":"2021","unstructured":"Qingyun Wu and Chi Wang. 2021. Fair AutoML. arXiv preprint arXiv:2111.06495 (2021)."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17239"},{"key":"e_1_3_2_1_10_1","volume-title":"ChaCha for Online AutoML. In International Conference on Machine Learning. PMLR, 11263--11273","author":"Wu Qingyun","year":"2021","unstructured":"Qingyun Wu, Chi Wang, John Langford, Paul Mineiro, and Marco Rossi. 2021 b. ChaCha for Online AutoML. In International Conference on Machine Learning. PMLR, 11263--11273."}],"event":{"name":"KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"],"location":"Washington DC USA","acronym":"KDD '22"},"container-title":["Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3542636","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3542636","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:59:55Z","timestamp":1750186795000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3542636"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,14]]},"references-count":10,"alternative-id":["10.1145\/3534678.3542636","10.1145\/3534678"],"URL":"https:\/\/doi.org\/10.1145\/3534678.3542636","relation":{},"subject":[],"published":{"date-parts":[[2022,8,14]]},"assertion":[{"value":"2022-08-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}