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ORCIDAutomated Machine Learning, 2019
- Frank Hutter, Lars Kotthoff, Joaquin Vanschoren
:
Automated Machine Learning - Methods, Systems, Challenges. The Springer Series on Challenges in Machine Learning, Springer 2019, ISBN 978-3-030-05317-8
AutoML Methods
- Matthias Feurer, Frank Hutter:
Hyperparameter Optimization. 3-33 - Joaquin Vanschoren:
Meta-Learning. 35-61 - Thomas Elsken, Jan Hendrik Metzen, Frank Hutter:
Neural Architecture Search. 63-77
AutoML Systems
- Lars Kotthoff, Chris Thornton, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown:
Auto-WEKA: Automatic Model Selection and Hyperparameter Optimization in WEKA. 81-95 - Brent Komer, James Bergstra, Chris Eliasmith:
Hyperopt-Sklearn. 97-111 - Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Tobias Springenberg, Manuel Blum, Frank Hutter:
Auto-sklearn: Efficient and Robust Automated Machine Learning. 113-134 - Hector Mendoza, Aaron Klein, Matthias Feurer, Jost Tobias Springenberg, Matthias Urban, Michael Burkart, Maximilian Dippel, Marius Lindauer, Frank Hutter:
Towards Automatically-Tuned Deep Neural Networks. 135-149 - Randal S. Olson, Jason H. Moore:
TPOT: A Tree-Based Pipeline Optimization Tool for Automating Machine Learning. 151-160 - Christian Steinruecken, Emma Smith, David Janz, James Robert Lloyd, Zoubin Ghahramani:
The Automatic Statistician. 161-173
AutoML Challenges
- Isabelle Guyon, Lisheng Sun-Hosoya, Marc Boullé, Hugo Jair Escalante, Sergio Escalera, Zhengying Liu, Damir Jajetic, Bisakha Ray, Mehreen Saeed, Michèle Sebag, Alexander R. Statnikov, Wei-Wei Tu, Evelyne Viegas:
Analysis of the AutoML Challenge Series 2015-2018. 177-219
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