TY  - CONF
AU  - Isabelle Guyon
AU  - Imad Chaabane
AU  - Hugo Jair Escalante
AU  - Sergio Escalera
AU  - Damir Jajetic
AU  - James Robert Lloyd
AU  - Nuria Macia
AU  - Bisakha Ray
AU  - Lukasz Romaszko
AU  - Michele Sebag
AU  - Alexander Statnikov
AU  - Sebastien Treguer
AU  - Evelyne Viegas
A2  - ICML
PY  - 2016//
TI  - A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention
BT  - AutoML Workshop
SP  - 1
EP  - 8
IS  - 1
KW  - AutoML Challenge
KW  - machine learning
KW  - model selection
KW  - meta-learning
KW  - repre- sentation learning
KW  - active learning
N2  - 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.
N1  - HuPBA;MILAB
ID  - Isabelle Guyon2016
ER  -