Automatic Machine Learning (AutoML)

F. Hutter 1 Balázs Kégl 2, 3, 4, 5 R. Caruana 6 I. Guyon 7 H. Larochelle 8 E. Viegas 6
2 AppStat
LAL - Laboratoire de l'Accélérateur Linéaire
5 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
Abstract : The success of machine learning in many domains crucially relies on human machine learning experts, who select appropriate features, workflows, machine learning paradigms, algorithms, and their hyperparameters. The rapid growth of machine learning applications has created a demand for off-the-shelf machine learning methods that can be used easily and without expert knowledge. We call the resulting research area that targets progressive automation of machine learning AutoML. For example, a recent instantiation of AutoML we’ll discuss is the ongoing ChaLearn AutoML challenge (http://codalab.org/AutoML).
Type de document :
Communication dans un congrès
ICML 2015 Workshop on Resource-Efficient Machine Learning, 32nd International Conference on Machine Learning, Jul 2015, Lille, France
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http://hal.in2p3.fr/in2p3-01171463
Contributeur : Sabine Starita <>
Soumis le : vendredi 3 juillet 2015 - 16:40:16
Dernière modification le : jeudi 5 avril 2018 - 12:30:12

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  • HAL Id : in2p3-01171463, version 1

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F. Hutter, Balázs Kégl, R. Caruana, I. Guyon, H. Larochelle, et al.. Automatic Machine Learning (AutoML) . ICML 2015 Workshop on Resource-Efficient Machine Learning, 32nd International Conference on Machine Learning, Jul 2015, Lille, France. 〈in2p3-01171463〉

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