@InProceedings{IsabelleGuyon2016,
author="Isabelle Guyon
and Imad Chaabane
and Hugo Jair Escalante
and Sergio Escalera
and Damir Jajetic
and James Robert Lloyd
and Nuria Macia
and Bisakha Ray
and Lukasz Romaszko
and Michele Sebag
and Alexander Statnikov
and Sebastien Treguer
and Evelyne Viegas",
title="A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention",
booktitle="AutoML Workshop",
year="2016",
number="1",
pages="1--8",
optkeywords="AutoML Challenge",
optkeywords="machine learning",
optkeywords="model selection",
optkeywords="meta-learning",
optkeywords="repre- sentation learning",
optkeywords="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.",
optnote="HuPBA;MILAB",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2769), last updated on Wed, 21 Jun 2017 09:10:29 +0200"
}