目錄
Part I AutoML Methods
1 Hyperparameter Optimization
Matthias Feurer and Frank Hutter
2 Meta-Learning
Joaquin Vanschoren
3 Neural Architecture Search .….
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter
Part II AutoML Systems
4 Auto-WEKA: Automatic Model Selection and Hyperparameter Optimization in WEKA..
Lars Kotthoff, Chris Thornton, Holger H. Hoos, Frank Hutter, and Kevin Leyton-Brown
5 Hyperopt-Sklearn.
Brent Komer, James Bergstra, and Chris Eliasmith
6 Auto-sklearn: Efficient and Robust Automated Machine Learning
Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Tobias Springenberg, Manuel Blum, and Frank Hutter
7 Towards Automatically-Tuned Deep Neural Networks.
Hector Mendoza, Aaron Klein, Matthias Feurer, Jost Tobias Springenberg, Matthias Urban, Michael Burkart, Maximilian Dippel, Marius Lindauer, and Frank Hutter
8 TPOT: A Tree-Based Pipeline Optimization Tool for Automating Machine Learning
Randal S. Olson and Jason H. Moore
9 The Automatic Statistician
Christian Steinruecken, Emma Smith, David Janz, James Lloyd, and Zoubin Ghahramani
Part III AutoML Challenges
10 Analysis of the AutoML Challenge Series 2015-2018 ….
Isabelle Guyon, Lisheng Sun-Hosoya, Marc Boull?, Hugo Jair Escalante, Sergio Escalera, Zhengying Liu, Damir Jajetic, Bisakha Ray, Mehreen Saeed, Mich?le Sebag, Alexander Statnikov, Wei-Wei Tu, and Evelyne Viegas
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