%0 Book Section
%T Analysis of the AutoML Challenge Series 2015-2018.
%A Isabelle Guyon
%A Lisheng Sun Hosoya
%A Marc Boulle
%A Hugo Jair Escalante
%A Sergio Escalera
%A Zhengying Liu
%A Damir Jajetic
%A Bisakha Ray
%A Mehreen Saeed
%A Michele Sebag
%A Alexander R.Statnikov
%A Wei-Wei Tu
%A Evelyne Viegas
%B Automated Machine Learning
%D 2019
%I Springer
%F Isabelle Guyon2019
%O HuPBA; no proj
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3330), last updated on Mon, 06 Mar 2023 15:46:05 +0100
%X The ChaLearn AutoML Challenge (The authors are in alphabetical order of last name, except the first author who did most of the writing and the second author who produced most of the numerical analyses and plots.) (NIPS 2015 – ICML 2016) consisted of six rounds of a machine learning competition of progressive difficulty, subject to limited computational resources. It was followed bya one-round AutoML challenge (PAKDD 2018). The AutoML setting differs from former model selection/hyper-parameter selection challenges, such as the one we previously organized for NIPS 2006: the participants aim to develop fully automated and computationally efficient systems, capable of being trained and tested without human intervention, with code submission. This chapter analyzes the results of these competitions and provides details about the datasets, which were not revealed to the participants. The solutions of the winners are systematically benchmarked over all datasets of all rounds and compared with canonical machine learning algorithms available in scikit-learn. All materials discussed in this chapter (data and code) have been made publicly available at http://automl.chalearn.org/.
%U https://link.springer.com/chapter/10.1007/978-3-030-05318-5_10
%P 177-219