National audienceWe give a brief account of the main findings of our post-hoc analysis of the first AutoML challenge (2015-2016). This competition, which took place in 2015-2016 challenged the participants to submit code that solve classification and regression problems from fixed-length feature representations, without any human intervention. This paper is a digest of a book chapter to be published in the Springer Series on Challenges in Machine Learning [Geaar]. All datasets, code of the winners, and challenge results are found at: http://automl.chalearn.org
International audienceFollowing the success of the first AutoML challenges , we designed a new chall...
International audienceThe success of machine learning in many domains crucially relies on human mach...
Preprint submitted to NeurIPS2018 Volume of Springer Series on Challenges in Machine LearningInterna...
National audienceWe give a brief account of the main findings of our post-hoc analysis of the first ...
International audienceThe ChaLearn AutoML Challenge 1 (NIPS 2015-ICML 2016) consisted of six rounds ...
International audienceWe present the design and results of recent competitions in Automated Deep Lea...
International audienceThe ChaLearn AutoML Challenge team conducted a large scale evaluation of fully...
International audienceThis paper reports the results and post-challenge analyses of ChaLearn’s AutoD...
ChaLearn is organizing the Automatic Machine Learning (AutoML) contest for IJCNN 2015, which challen...
This open access book presents the first comprehensive overview of general methods in Automated Mach...
This open access book presents the first comprehensive overview of general methods in Automatic Mach...
International audienceFollowing the success of the first AutoML challenges , we designed a new chall...
International audienceThe success of machine learning in many domains crucially relies on human mach...
Preprint submitted to NeurIPS2018 Volume of Springer Series on Challenges in Machine LearningInterna...
National audienceWe give a brief account of the main findings of our post-hoc analysis of the first ...
International audienceThe ChaLearn AutoML Challenge 1 (NIPS 2015-ICML 2016) consisted of six rounds ...
International audienceWe present the design and results of recent competitions in Automated Deep Lea...
International audienceThe ChaLearn AutoML Challenge team conducted a large scale evaluation of fully...
International audienceThis paper reports the results and post-challenge analyses of ChaLearn’s AutoD...
ChaLearn is organizing the Automatic Machine Learning (AutoML) contest for IJCNN 2015, which challen...
This open access book presents the first comprehensive overview of general methods in Automated Mach...
This open access book presents the first comprehensive overview of general methods in Automatic Mach...
International audienceFollowing the success of the first AutoML challenges , we designed a new chall...
International audienceThe success of machine learning in many domains crucially relies on human mach...
Preprint submitted to NeurIPS2018 Volume of Springer Series on Challenges in Machine LearningInterna...