%0 Conference Proceedings
%T AutoML Challenge 2015: Design and First Results
%A Isabelle Guyon
%A Kristin Bennett
%A Gavin Cawley
%A Hugo Jair Escalante
%A Sergio Escalera
%A Tin Kam Ho
%A Nuria Macia
%A Bisakha Ray
%A Mehreen Saeed
%A Alexander Statnikov
%A Evelyne Viegas
%B 32nd International Conference on Machine Learning, ICML workshop, JMLR proceedings ICML15
%D 2015
%F Isabelle Guyon2015
%O HuPBA;MILAB
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2656), last updated on Thu, 12 May 2016 17:35:26 +0200
%X 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 canenter 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.
%K AutoML Challenge
%K machine learning
%K model selection
%K meta-learning
%K repre- sentation learning
%K active learning
%U http://jmlr.org/proceedings/papers/v37
%U http://dx.doi.org/10.1109/IJCNN.2015.7280767
%P 1-8