Design of the 2015 chalearn automl challenge



ChaLearn is organizing the Automatic Machine Learning (AutoML) contest for IJCNN 2015, which challenges participants to solve classification 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 difficulty 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, and phases in which participants have limited time to tweak their algorithms on those datasets to improve performance. This challenge will push the state of the art in fully automatic machine learning on a wide range of real-world problems. The platform …


Isabelle Guyon
Kristin P. Bennett
Sergio Escalera
Gavin C Cawley
Alexander Statnikov
Tin Kam Ho
Núria Macià
Bisakha Ray
Mehreen Saeed
Isabelle Guyon
Evelyne Viegas