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https://doi.org/10.1007/s10994-022-06200-0</unstructured_citation>
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SIGKDD International Conference on Knowledge Discovery &amp; Data Mining
(KDD’19), 2623–2631.
https://doi.org/10.1145/3292500.3330701</unstructured_citation>
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the 19th ACM SIGKDD International Conference on Knowledge Discovery
&amp; Data Mining (KDD’13), 847–855.
https://doi.org/10.1007/978-3-030-05318-5_4</unstructured_citation>
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computation conference 2016</journal_title>
<doi>10.1145/2908812.2908918</doi>
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R., &amp; Moore, J. (2016). Evaluation of a tree-based pipeline
optimization tool for automating data science. Proceedings of the
Genetic and Evolutionary Computation Conference 2016, 485–492.
https://doi.org/10.1145/2908812.2908918</unstructured_citation>
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learning</article_title>
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<cYear>2022</cYear>
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diagnostic and prognostic modeling in healthcare with automated machine
learning. arXiv:2210.12090 [Cs.LG].
https://doi.org/10.1371/journal.pdig.0000276</unstructured_citation>
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Systems</journal_title>
<volume>3</volume>
<cYear>2021</cYear>
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Proceedings of Machine Learning and Systems, 3,
434–447.</unstructured_citation>
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systems (NeurIPS)</journal_title>
<cYear>2021</cYear>
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Ram, P., Shinnar, A., &amp; Tsay, J. (2021). Pipeline combinators for
gradual AutoML. Advances in Neural Information Processing Systems
(NeurIPS), 19705–19718.
https://proceedings.neurips.cc/paper/2021/file/a3b36cb25e2e0b93b5f334ffb4e4064e-Paper.pdf</unstructured_citation>
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learning</article_title>
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<journal_title>7th ICML Workshop on Automated Machine
Learning (AutoML)</journal_title>
<cYear>2020</cYear>
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H2O AutoML: Scalable automatic machine learning. 7th ICML Workshop on
Automated Machine Learning (AutoML).
https://www.automl.org/wp-content/uploads/2020/07/AutoML_2020_paper_61.pdf</unstructured_citation>
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