[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118651-en":3,"doc-seo-118651-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118651,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Towards Green Automated Machine Learning - Status Quo and Future Directions","Automated machine learning (AutoML) automatically configures learning algorithms and assembles them into full machine-learning pipelines tailored to a dataset and task. With AutoML’s rapid growth, criticism focuses on high resource consumption from evaluating many pipelines and conducting large-scale experiments. In the context of Green AI, this work introduces Green AutoML as a paradigm for improving environmental sustainability. It explains how to quantify footprint, summarizes design and benchmarking strategies, and proposes transparency practices plus a sustainability checklist for AutoML papers.","Towards Green Automated Machine Learning: Status Quo and Future Directions  \nTanja Tornede tanja.tornede@upb.de  \nAlexander Tornede alexander.tornede@upb.de  \nJonas Hanselle [jonas.hanselle@upb.de](jonas.hanselle@upb.de)  \nDepartment of Computer Science, Paderborn University, Germany  \nFelix Mohr [felix.mohr@unisabana.edu.co](felix.mohr@unisabana.edu.co)  \nUniversidad de La Sabana, Chia, Cundinamarca, Colombia  \nMarcel Wever [marcel.wever@ifi.lmu.de](marcel.wever@ifi.lmu.de)  \nEyke H􀁿ullermeier [eyke@ifi.lmu.de](eyke@ifi.lmu.de)  \nInstitute of Informatics, University of Munich (LMU), Germany Munich Center for Machine Learning (MCML), Germany  \nAbstract  \nAutomated machine learning (AutoML) strives for the automatic con􀀌guration 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 􀀌eld 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 􀀌rst elaborate on how to quantify the environmental footprint of an AutoML tool. Afterward, di􀀋erent 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.  \n1. Introduction  \nA machine learning (ML) pipeline is a combination of suitably con􀀌gured ML algorithms into an overall (software) solution that can be applied to a speci􀀌c learning task, which is typically characterized by a dataset on which a (predictive) model ought to be trained. The design of such pipelines is a time and resource consuming task due to the immense number of solutions conceivable, each of them solving the same problem but varying in performance (i.e., producing models of better or worse predictive accuracy) . Therefore, aiming to 􀀌nd the best performing pipeline, candidates are evaluated on the dataset at hand to further adapt and improve its composition and con􀀌guration. Aiming at the automation of this process, AutoML (Hutter et al., 2019) develops methods for searching the space of ML pipelines in a systematic way, typically with a prede􀀌ned timeout after which the most  \n©2023 The Authors. Published by AI Access Foundation under Creative Commons Attribution License CC BY 4.0 .  \nTornede, Tornede, Hanselle, Wever, Mohr, & Hllermeier  \npromising candidate is used to train the 􀀌nal model. The interest in the 􀀌eld of AutoML has rapidly increased in the recent past, and the 􀀌eld has broadened in scope, though predictive performance remains the main measure of interest.  \nIn the 􀀌eld of AutoML, theoretical results are quite di􀀎cult to obtain, because the problem is complex and hardly amenable to theoretical analysis. Accordingly, most research contributions are of empirical nature: the proposition of a new technique or approach is accompanied by large experimental studies to show the bene􀀌ts of the proposed techniques. As one is interested in techniques that work well in general, across a broad range of problems, a large number of datasets is required for evaluation. Additionally, as there is not a single AutoML system representing the state ofthe art (SOTA) and dominating all others, multiple competitors need to be assessed. To this end, each competitor is u","cbCaiqziOdJ88HcK","https://ap.wps.com/l/cbCaiqziOdJ88HcK","pdf",299434,1,31,"English","en",105,"# Introduction\n## AutoML pipelines and resource demands\n## Carbon emissions and evaluation focus","[{\"question\":\"什么是 Green AutoML，为什么需要它？\",\"answer\":\"Green AutoML旨在让AutoML的整个过程更具环境友好性，回应AutoML在大量流水线评估与大规模实验中带来的高资源消耗与碳排放问题。\"},{\"question\":\"论文如何量化AutoML工具的环境足迹？\",\"answer\":\"论文先讨论如何度量AutoML工具的环境足迹，为后续的“绿色”设计、基准评测与透明报告提供依据。\"},{\"question\":\"论文提出哪些策略与机制来推动更可持续的AutoML研究？\",\"answer\":\"论文总结了围绕“greenness”（可持续性）的设计与基准策略，并强调在环境足迹披露方面的透明做法，同时提出可附在每篇AutoML论文中的可持续性检查清单来引导研究激励。\"}]","Towards Green Automated Machine Learning - Status Quo and Future Directions | PDF",1785684720,78,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"towards-green-automated-machine-learning-status-quo-and-future-directions","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/towards-green-automated-machine-learning-status-quo-and-future-directions/118651/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"什么是 Green AutoML，为什么需要它？","Question",{"text":76,"@type":77},"Green AutoML旨在让AutoML的整个过程更具环境友好性，回应AutoML在大量流水线评估与大规模实验中带来的高资源消耗与碳排放问题。","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"论文如何量化AutoML工具的环境足迹？",{"text":81,"@type":77},"论文先讨论如何度量AutoML工具的环境足迹，为后续的“绿色”设计、基准评测与透明报告提供依据。",{"name":83,"@type":74,"acceptedAnswer":84},"论文提出哪些策略与机制来推动更可持续的AutoML研究？",{"text":85,"@type":77},"论文总结了围绕“greenness”（可持续性）的设计与基准策略，并强调在环境足迹披露方面的透明做法，同时提出可附在每篇AutoML论文中的可持续性检查清单来引导研究激励。","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]