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Author (up) Isabelle Guyon; Kristin Bennett; Gavin Cawley; Hugo Jair Escalante; Sergio Escalera; Tin Kam Ho; Nuria Macia; Bisakha Ray; Mehreen Saeed; Alexander Statnikov; Evelyne Viegas edit  url
doi  openurl
  Title AutoML Challenge 2015: Design and First Results Type Conference Article
  Year 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  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  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 @ GBC2015c Serial 2656  
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