Analysis of the AutoML Challenge series 2015-2018
| Autor: | Guyon, Isabelle, Sun-Hosoya, Lisheng, Boullé, Marc, Escalante, Hugo, Escalera, Sergio, Liu, Zhengying, Jajetic, Damir, Ray, Bisakha, Saeed, Mehreen, Sebag, Michèle, Statnikov, Alexander, Tu, Wei-Wei, Viegas, Evelyne |
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| Přispěvatelé: | TAckling the Underspecified (TAU), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Chalearn, Orange Labs [Lannion], France Télécom, Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Computer Vision Center (Centre de visio per computador) (CVC), Universitat Autònoma de Barcelona (UAB), Université Paris-Sud - Paris 11 (UP11), IN2 [Zagreb], New York University Langone Medical Center (NYU Langone Medical Center), NYU System (NYU), Foundation for Advancement of Science and Technology (NUCES | FAST Karachi), National University of Science and Technology [Bulawayo], Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), New York University School of Medicine (NYU), New York University School of Medicine, NYU System (NYU)-NYU System (NYU), 4Paradigm, Microsoft Corporation [Redmond], Microsoft Corporation [Redmond, Wash.], Frank Hutter, Lars Kotthoff, Joaquin Vanschoren |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: | |
| Zdroj: | AutoML: Methods, Systems, Challenges Frank Hutter; Lars Kotthoff; Joaquin Vanschoren. AutoML: Methods, Systems, Challenges, Springer Verlag, In press, The Springer Series on Challenges in Machine Learning |
| Popis: | International audience; The ChaLearn AutoML Challenge 1 (NIPS 2015-ICML 2016) consisted of six rounds of a machine learning competition of progressive difficulty, subject to limited computational resources. It was followed by one round of AutoML challenge (PAKDD 2018). The AutoML setting differs from former model selection/hyper-parameter selection challenges, such as the one we previously organized for NIPS 2006: the participants aim to develop fully automated and computationally efficient systems, capable of being trained and tested without human intervention, with code submission. This paper analyzes the results of these competitions and provides details about the datasets, which were not revealed to the participants. The solutions of the winners are systematically benchmarked over all datasets of all rounds and compared with canonical machine learning algorithms available in scikit-learn. All materials discussed in this paper (data and code) have been made publicly available at http://automl.chalearn.org/. |
| Databáze: | OpenAIRE |
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