AutoML @ NeurIPS 2018 Challenge: Design and Results

Author:

Escalante Hugo Jair,Tu Wei-Wei,Guyon Isabelle,Silver Daniel L.,Viegas Evelyne,Chen Yuqiang,Dai Wenyuan,Yang Qiang

Publisher

Springer International Publishing

Reference17 articles.

1. J. Q. Candela, N. Lawrence, and A. Schwaighofer. Pascale 2 challenge: Learning when test and training inputs have different distributions challenge, 2005.

2. Zhiyuan Chen and Bing Liu. Lifelong Machine Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2016.

3. Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, and Frank Hutter. Efficient and robust automated machine learning. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, editors, Advances in Neural Information Processing Systems 28, pages 2962–2970. Curran Associates, Inc., 2015.

4. Isabelle Guyon, Kristin Bennett, Gavin C. Cawley, Hugo Jair Escalante, Sergio Escalera, Tin Kam Ho, Núria Macià, Bisakha Ray, Mehreen Saeed, Alexander R. Statnikov, and Evelyne Viegas. Design of the 2015 chalearn automl challenge. In 2015 International Joint Conference on Neural Networks, IJCNN 2015, Killarney, Ireland, July 12–17, 2015, pages 1–8, 2015.

5. Isabelle Guyon, Imad Chaabane, Hugo Jair Escalante, Sergio Escalera, Damir Jajetic, James Robert Lloyd, Núria Macià, Bisakha Ray, Lukasz Romaszko, Michèle Sebag, Alexander R. Statnikov, Sébastien Treguer, and Evelyne Viegas. A brief review of the chalearn automl challenge: Any-time any-dataset learning without human intervention. In Proceedings of the 2016 Workshop on Automatic Machine Learning, AutoML 2016, co-located with 33rd International Conference on Machine Learning (ICML 2016), New York City, NY, USA, June 24, 2016, pages 21–30, 2016.

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