迈向绿色自动化机器学习：现状与未来方向
Towards Green Automated Machine Learning: Status Quo and Future Directions
AI总结：
针对AutoML资源消耗高的问题，提出Green AutoML范式，涵盖环境足迹量化、绿色设计与基准测试策略、透明化与激励机制，并附可持续性检查清单，推动AutoML可持续发展。
摘要：
自动化机器学习（AutoML）致力于自动配置机器学习算法，并将其组合为适配当前学习任务（数据集）的整体（软件）解决方案——即机器学习流水线。过去十年间，AutoML已发展为一个独立研究领域，相关成果达数百项。与此同时，AutoML因资源消耗高受到批评，许多方法依赖对大量机器学习流水线的（高成本）评估，还需开展跨多个数据集与方法的昂贵大规模实验。\n秉承近期绿色AI相关研究的理念，本文提出Green AutoML（绿色自动化机器学习）范式，旨在让整个AutoML过程更具环境友好性。为此，我们首先阐述了如何量化AutoML工具的环境足迹。随后，总结了围绕“绿色性”（即可持续性）设计与基准测试AutoML工具的不同策略。最后，我们阐释了如何实现环境足迹的透明化，以及何种研究激励机制能够引导学界迈向更具可持续性的AutoML研究方向。此外，我们提出了一份可持续性检查清单，可附在每篇AutoML论文后，涵盖Green AutoML的所有核心方面。
英文摘要：
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 criticised 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 wrt. 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 into a more sustainable AutoML research direction. Additionally, we propose a sustainability checklist to be attached to every AutoML paper featuring all core aspects of Green AutoML.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
