@InProceedings{IsabelleGuyon2015,
author="Isabelle Guyon
and Kristin Bennett
and Gavin Cawley
and Hugo Jair Escalante
and Sergio Escalera
and Tin Kam Ho
and Nuria Macia
and Bisakha Ray
and Alexander Statnikov
and Evelyne Viegas",
title="Design of the 2015 ChaLearn AutoML Challenge",
booktitle="IEEE International Joint Conference on Neural Networks IJCNN2015",
year="2015",
abstract="ChaLearn is organizing for IJCNN 2015 an Automatic Machine Learning challenge (AutoML) to solve classification and regression problems from given feature representations, without any human intervention. This is a challenge with codesubmission: the code submitted can be executed automatically on the challenge servers to train and test learning machines on new datasets. However, there is no obligation to submit code. Half of the prizes can be won by just submitting prediction results.There are six rounds (Prep, Novice, Intermediate, Advanced, Expert, and Master) in which datasets of progressive difficulty are introduced (5 per round). There is no requirement to participate in previous rounds to enter a new round. The rounds alternate AutoML phases in which submitted code is {\textquoteleft}{\textquoteleft}blind tested{\textquoteright}{\textquoteright} ondatasets the participants have never seen before, and Tweakathon phases giving time ({\textquoteright} 1 month) to the participants to improve their methods by tweaking their code on those datasets. This challenge will push the state-of-the-art in fully automatic machine learning on a wide range of problems taken from real worldapplications. The platform will remain available beyond the termination of the challenge: http://codalab.org/AutoML",
optnote="HuPBA;MILAB",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2604), last updated on Thu, 12 May 2016 15:50:40 +0200",
opturl="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7256526"
}