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Author Isabelle Guyon; Kristin Bennett; Gavin Cawley; Hugo Jair Escalante; Sergio Escalera; Tin Kam Ho; Nuria Macia; Bisakha Ray; Mehreen Saeed; Alexander Statnikov; Evelyne Viegas
Title AutoML Challenge 2015: Design and First Results Type Conference Article
Year (up) 2015 Publication 32nd International Conference on Machine Learning, ICML workshop, JMLR proceedings ICML15 Abbreviated Journal
Volume Issue Pages 1-8
Keywords AutoML Challenge; machine learning; model selection; meta-learning; repre- sentation learning; active learning
Abstract ChaLearn is organizing the Automatic Machine Learning (AutoML) contest 2015, which challenges participants to solve classi cation and regression problems without any human intervention. Participants' code is automatically run on the contest servers to train and test learning machines. However, there is no obligation to submit code; half of the prizes can be won by submitting prediction results only. Datasets of progressively increasing diculty are introduced throughout the six rounds of the challenge. (Participants can
enter the competition in any round.) The rounds alternate phases in which learners are tested on datasets participants have not seen (AutoML), and phases in which participants have limited time to tweak their algorithms on those datasets to improve performance (Tweakathon). This challenge will push the state of the art in fully automatic machine learning on a wide range of real-world problems. The platform will remain available beyond the termination of the challenge: http://codalab.org/AutoML.
Address Lille; France; July 2015
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Area Expedition Conference ICML
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ GBC2015c Serial 2656
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Author Isabelle Guyon; Imad Chaabane; Hugo Jair Escalante; Sergio Escalera; Damir Jajetic; James Robert Lloyd; Nuria Macia; Bisakha Ray; Lukasz Romaszko; Michele Sebag; Alexander Statnikov; Sebastien Treguer; Evelyne Viegas
Title A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention Type Conference Article
Year (up) 2016 Publication AutoML Workshop Abbreviated Journal
Volume Issue 1 Pages 1-8
Keywords AutoML Challenge; machine learning; model selection; meta-learning; repre- sentation learning; active learning
Abstract The ChaLearn AutoML Challenge team conducted a large scale evaluation of fully automatic, black-box learning machines for feature-based classification and regression problems. The test bed was composed of 30 data sets from a wide variety of application domains and ranged across different 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 specific constraints and sharing their approaches; the platform will remain available for post-challenge submissions at http://codalab.org/AutoML.
Address New York; USA; June 2016
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICML
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ GCE2016 Serial 2769
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