PT Unknown
AU Isabelle Guyon
   Kristin Bennett
   Gavin Cawley
   Hugo Jair Escalante
   Sergio Escalera
   Tin Kam Ho
   Nuria Macia
   Bisakha Ray
   Mehreen Saeed
   Alexander Statnikov
   Evelyne Viegas
TI AutoML Challenge 2015: Design and First Results
BT 32nd International Conference on Machine Learning, ICML workshop, JMLR proceedings ICML15
PY 2015
BP 1
EP 8
DI 10.1109/IJCNN.2015.7280767
DE AutoML Challenge; machine learning; model selection; meta-learning; repre- sentation learning; active learning
AB ChaLearn is organizing the Automatic Machine Learning (AutoML) contest 2015, which challenges participants to solve classication 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 diculty are introduced throughout the six rounds of the challenge. (Participants canenter the competition in any round.) The rounds alternate phases in which learners are tested on datasets participants have not seen (AutoML), and phases in which participants have limited time to tweak their algorithms on those datasets to improve performance (Tweakathon). 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: http://codalab.org/AutoML.
ER