A Brief Review of the ChaLearn AutoML Challenge: Any-Time Any-Dataset Learning Without Human Intervention
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 ranging across different types of complexity. Over five 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 challenge has been a leap forward in the field and its platform will remain available for post-challenge submissions at http://codalab.org/AutoML.
Cite
Text
Guyon et al. "A Brief Review of the ChaLearn AutoML Challenge: Any-Time Any-Dataset Learning Without Human Intervention." Proceedings of the Workshop on Automatic Machine Learning, 2016.Markdown
[Guyon et al. "A Brief Review of the ChaLearn AutoML Challenge: Any-Time Any-Dataset Learning Without Human Intervention." Proceedings of the Workshop on Automatic Machine Learning, 2016.](https://mlanthology.org/automl/2016/guyon2016automl-brief/)BibTeX
@inproceedings{guyon2016automl-brief,
title = {{A Brief Review of the ChaLearn AutoML Challenge: Any-Time Any-Dataset Learning Without Human Intervention}},
author = {Guyon, Isabelle and Chaabane, Imad and Escalante, Hugo Jair and Escalera, Sergio and Jajetic, Damir and Lloyd, James Robert and Macià, Núria and Ray, Bisakha and Romaszko, Lukasz and Sebag, Michèle and Statnikov, Alexander and Treguer, Sébastien and Viegas, Evelyne},
booktitle = {Proceedings of the Workshop on Automatic Machine Learning},
year = {2016},
pages = {21-30},
volume = {64},
url = {https://mlanthology.org/automl/2016/guyon2016automl-brief/}
}