Winning Software



Only 2 phases:

    1. Feed-back phase: You get to practice (with code or result submission) on 5 datasets and get immediate feed-back on a leaderboard.
    2. AutoML phase: The code gets blind tested on 5 new datasets.

The setting is similar to AutoML1, so you can get a head start by studying what was done.


The First ChaLearn AutoML challenge included 5 rounds, 30 datasets (available for download), and $30,000 in prizes. The goal was to design the perfect machine learning “black box” capable of performing all model selection and hyper-parameter tuning without any human intervention. Post-challenge submissions can be made on the clone websites:

In phase Final4, we also had a GPU track.


The results of top ranking participants of the first round were discussed at the AutoML workshop at ICML 2015. See summary paper. A more detailed paper was presented at IJCNN 2015. The NIPS 2016 [slides] give an update. We also have published the results of a short survey on methods used (fact sheets). Thanks for citing our papers, see citations for the BibTex entries.

The top ranking participants described briefly their methods in the FACT SHEETS and wrote blogs:


The competition started December 8, 2014 and ended May 1, 2016.