Towards Green Automated Machine Learning: Status Quo and Future Directions

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Autoren

Externe Organisationen

  • Universität Paderborn
  • Universidad de la Sabana
  • Ludwig-Maximilians-Universität München (LMU)
  • Munich Center for Machine Learning (MCML)
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Details

OriginalspracheEnglisch
Seiten (von - bis)427-457
Seitenumfang31
FachzeitschriftJournal of Artificial Intelligence Research
Jahrgang77
PublikationsstatusVeröffentlicht - 2023
Extern publiziertJa

Abstract

Automated machine learning (AutoML) strives for the automatic configuration of machine learning algorithms and their composition into an overall (software) solution - a machine learning pipeline - tailored to the learning task (dataset) at hand. Over the last decade, AutoML has developed into an independent research field with hundreds of contributions. At the same time, AutoML is being criticized for its high resource consumption as many approaches rely on the (costly) evaluation of many machine learning pipelines, as well as the expensive large-scale experiments across many datasets and approaches. In the spirit of recent work on Green AI, this paper proposes Green AutoML, a paradigm to make the whole AutoML process more environmentally friendly. Therefore, we first elaborate on how to quantify the environmental footprint of an AutoML tool. Afterward, different strategies on how to design and benchmark an AutoML tool w.r.t. their "greenness", i.e., sustainability, are summarized. Finally, we elaborate on how to be transparent about the environmental footprint and what kind of research incentives could direct the community in a more sustainable AutoML research direction. As part of this, we propose a sustainability checklist to be attached to every AutoML paper featuring all core aspects of Green AutoML.

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Towards Green Automated Machine Learning: Status Quo and Future Directions. / Tornede, Tanja; Tornede, Alexander; Hanselle, Jonas et al.
in: Journal of Artificial Intelligence Research, Jahrgang 77, 2023, S. 427-457.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Tornede T, Tornede A, Hanselle J, Mohr F, Wever M, Hüllermeier E. Towards Green Automated Machine Learning: Status Quo and Future Directions. Journal of Artificial Intelligence Research. 2023;77:427-457. doi: 10.1613/jair.1.14340
Tornede, Tanja ; Tornede, Alexander ; Hanselle, Jonas et al. / Towards Green Automated Machine Learning : Status Quo and Future Directions. in: Journal of Artificial Intelligence Research. 2023 ; Jahrgang 77. S. 427-457.
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