A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention

The AutoML challenge has ended and they have a paper on the results.

A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention by Isabelle Guyon, Imad Chaabane, Hugo Jair Escalante, Sergio Escalera, Damir Jajetic, James Robert Lloyd, Nuria Macia, Bisakha Ray, Lukasz Romaszko, Michele Sebag, Alexander Statnikov, Sébastien Treguer, Evelyne Viegas

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.

Previously:

• Sunday Morning Insight: The Hardest Challenges We Should be Unwilling to Postpone
• Paris Machine Learning Meetup #5 Season 3, Sebastien Treguer, AutoML Challenge, what's that? AutoML, Successes & Challenges ( Bayesian optimisation, Ensemble construction, Sensible allocation of computation)

Join the CompressiveSensing subreddit or the Google+ Community or the Facebook page and post there !
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.