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4 results on '"Jajetic, Damir"'

1. Analysis of the AutoML Challenge Series 2015–2018

Author

Guyon, Isabelle, Sun-Hosoya, Lisheng, Boullé, Marc, Escalante, Hugo Jair, Escalera, Sergio, Liu, Zhengying, Jajetic, Damir, Ray, Bisakha, Saeed, Mehreen, Sebag, Michèle, Statnikov, Alexander, Tu, Wei-Wei, Viegas, Evelyne, Escalante, Hugo Jair, Series Editor, Guyon, Isabelle, Series Editor, Escalera, Sergio, Series Editor, Hutter, Frank, editor, Kotthoff, Lars, editor, and Vanschoren, Joaquin, editor

Published
2019
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2. Analysis of the AutoML Challenge Series 2015–2018

Author

Guyon, Isabelle, primary, Sun-Hosoya, Lisheng, additional, Boullé, Marc, additional, Escalante, Hugo Jair, additional, Escalera, Sergio, additional, Liu, Zhengying, additional, Jajetic, Damir, additional, Ray, Bisakha, additional, Saeed, Mehreen, additional, Sebag, Michèle, additional, Statnikov, Alexander, additional, Tu, Wei-Wei, additional, and Viegas, Evelyne, additional

Published
2019
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3. Analysis of the AutoML Challenge series 2015-2018

Author

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, 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, and Joaquin Vanschoren

Subjects
[STAT.ML]Statistics [stat]/Machine Learning [stat.ML]
Abstract

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/.

Published
2018

4. A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention

Author

Guyon, Isabelle, Chaabane, Imad, Escalante, Hugo Jair, Escalera, Sergio, Jajetic, Damir, Lloyd, James Robert, Macia, Nuria, Ray, Bisakha, Romaszko, Lukasz, Sebag, Michèle, Statnikov, Alexander, Treguer, Sebastien, Viegas, Evelyne, Chalearn, Machine Learning and Optimisation (TAO), 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)-Université Paris-Sud - Paris 11 (UP11)-Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec, Université Paris-Sud - Paris 11 - Faculté des Sciences (UP11 UFR Sciences), Université Paris-Sud - Paris 11 (UP11), Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Computer Vision Center (Centre de visio per computador) (CVC), Universitat Autònoma de Barcelona (UAB), IN2 [Zagreb], Qlearsite [London], Universitat d'Andorra, New York University Langone Medical Center (NYU Langone Medical Center), NYU System (NYU), School of Informatics [Edimbourg], University of Edinburgh, New York University School of Medicine (NYU), New York University School of Medicine, NYU System (NYU)-NYU System (NYU), IBM, Microsoft Corporation [Redmond], Microsoft Corporation [Redmond, Wash.], 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, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; The ChaLearn AutoML Challenge team conducted a large scale evaluation of fully auto-matic, black-box learning machines for feature-based classi cation and regression problems. The test bed was composed of 30 data sets from a wide variety of application domains and ranged across di erent types of complexity. Over six rounds, participants succeeded in delivering AutoML software capable of being trained and tested without human intervention. Although improvements can still be made to close the gap between human-tweaked and AutoML models, this competition contributes to the development of fully automated environments by challenging practitioners to solve problems under speci c constraints and sharing their approaches; the platform will remain available for post-challenge submissions at http://codalab.org/AutoML.

Published
2016

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