A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention

Abstract : The ChaLearn AutoML Challenge team conducted a large scale evaluation of fully auto- matic, black-box learning machines for feature-based classi cation and regression problems. The test bed was composed of 30 data sets from a wide variety of application domains and ranged across di erent types of complexity. Over six rounds, participants succeeded in delivering AutoML software capable of being trained and tested without human intervention. Although improvements can still be made to close the gap between human-tweaked and AutoML models, this competition contributes to the development of fully automated environments by challenging practitioners to solve problems under speci c constraints and sharing their approaches; the platform will remain available for post-challenge submissions at http://codalab.org/AutoML.
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Communication dans un congrès
International Conference in Machine Learning (ICML 2016) Workshops, 2016, New-York, United States. JMLR, JMLR: Workshop and Conference Proceedings 1, pp.1-8, 2016, ICML 2016 AutoML Workshop
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https://hal.archives-ouvertes.fr/hal-01381145
Contributeur : Isabelle Guyon <>
Soumis le : vendredi 14 octobre 2016 - 09:29:53
Dernière modification le : samedi 12 janvier 2019 - 11:56:01

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  • HAL Id : hal-01381145, version 1

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Isabelle Guyon, Imad Chaabane, Hugo Jair Escalante, Sergio Escalera, Damir Jajetic, et al.. A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention. International Conference in Machine Learning (ICML 2016) Workshops, 2016, New-York, United States. JMLR, JMLR: Workshop and Conference Proceedings 1, pp.1-8, 2016, ICML 2016 AutoML Workshop. 〈hal-01381145〉

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