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It could release the burden of data scientists from the multifarious manual tuning process and enable the access of domain experts to the off-the-shelf machine learning solutions without extensive experience. In this paper, we portray AutoML as a bi-level optimization problem, where one problem is nested within another to search the optimum in the search space, and review the current developments of AutoML in terms of three categories, automated feature engineering (AutoFE), automated model and hyperparameter tuning (AutoMHT), and automated deep learning (AutoDL). Stateof- the-art techniques in the three categories are presented. The iterative solver is proposed to generalize AutoML techniques. We summarize popular AutoML frameworks and conclude with current open challenges of AutoML.<\/jats:p>","DOI":"10.1145\/3447556.3447567","type":"journal-article","created":{"date-parts":[[2021,1,17]],"date-time":"2021-01-17T23:08:00Z","timestamp":1610924880000},"page":"35-50","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":43,"title":["Techniques for Automated Machine Learning"],"prefix":"10.1145","volume":"22","author":[{"given":"Yi-Wei","family":"Chen","sequence":"first","affiliation":[{"name":"Texas A&amp;M University, College Station, TX, USA"}]},{"given":"Qingquan","family":"Song","sequence":"additional","affiliation":[{"name":"Texas A&amp;M University, College Station, TX, USA"}]},{"given":"Xia","family":"Hu","sequence":"additional","affiliation":[{"name":"Texas A&amp;M University, College Station, TX, USA"}]}],"member":"320","published-online":{"date-parts":[[2021,1,17]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Amazon. 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