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dc.contributor.author
Li, Yaliang
dc.contributor.author
Wang, Zhen
dc.contributor.author
Xie, Yuexiang
dc.contributor.author
Ding, Bolin
dc.contributor.author
Zeng, Kai
dc.contributor.author
Zhang, Ce
dc.date.accessioned
2021-11-25T14:08:13Z
dc.date.available
2021-11-23T04:04:14Z
dc.date.available
2021-11-25T14:08:13Z
dc.date.issued
2021-10
dc.identifier.isbn
978-1-4503-8446-9
en_US
dc.identifier.other
10.1145/3459637.3483279
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/516415
dc.description.abstract
Machine Learning methods have been adopted for a wide range of real-world applications, ranging from social networks, online image/video-sharing platforms, and e-commerce to education, healthcare, etc. However, in practice, a large amount of effort is required to tune several components of machine learning methods, including data representation, hyperparameter, and model architecture, in order to achieve a good performance. To alleviate the required tunning efforts, Automated Machine Learning (AutoML), which can automate the process of applying machine learning methods, has been studied in both academy and industry recently. In this tutorial, we will introduce the main research topics of AutoML, including Hyperparameter Optimization, Neural Architecture Search, and Meta-Learning. Two emerging topics of AutoML, Automatic Feature Generation and Machine Learning Guided Database, will also be discussed since they are important components for real-world applications. For each topic, we will motivate it with application examples from industry, illustrate the state-of-the-art methodologies, and discuss some future research directions based on our experience from industry and the trends in academy. © 2021 ACM
en_US
dc.language.iso
en
en_US
dc.publisher
Association for Computing Machinery
dc.title
AutoML: From Methodology to Application
en_US
dc.type
Conference Paper
dc.date.published
2021-10-26
ethz.book.title
Proceedings of the 30th ACM International Conference on Information & Knowledge Management
en_US
ethz.pages.start
4853
en_US
ethz.pages.end
4856
en_US
ethz.event
30th ACM International Conference on Information and Knowledge Management (CIKM '21)
en_US
ethz.event.location
Online
ethz.event.date
November 1-5, 2021
en_US
ethz.identifier.scopus
ethz.publication.place
New York, NY
ethz.publication.status
published
en_US
ethz.date.deposited
2021-11-23T04:04:24Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2021-11-25T14:08:19Z
ethz.rosetta.lastUpdated
2024-02-02T15:26:58Z
ethz.rosetta.versionExported
true
ethz.COinS
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