Design of the 2015 ChaLearn AutoML challenge

Isabelle Guyon (Lead Author), Gavin Cawley, Kristin Bennett, Hugo Jair Escalente, Sergio Escalera, Tin Kam Ho, Nuria Macia, Bisakha Ray, Mehreen Saeed, Alexander Statnikov, Evelyne Viegas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

37 Citations (Scopus)

Abstract

ChaLearn is organizing the Automatic Machine Learning (AutoML) contest for IJCNN 2015, which challenges participants to solve classification and regression problems without any human intervention. Participants' code is automatically run on the contest servers to train and test learning machines. However, there is no obligation to submit code; half of the prizes can be won by submitting prediction results only. Datasets of progressively increasing difficulty are introduced throughout the six rounds of the challenge. (Participants can enter the competition in any round.) The rounds alternate phases in which learners are tested on datasets participants have not seen, and phases in which participants have limited time to tweak their algorithms on those datasets to improve performance. This challenge will push the state of the art in fully automatic machine learning on a wide range of real-world problems. The platform will remain available beyond the termination of the challenge.
Original languageEnglish
Title of host publicationProceedings of International Joint Conference on Neural Networks (IJCNN)
PublisherIEEE Press
Number of pages8
DOIs
Publication statusPublished - 12 Jul 2015

Cite this