Part I AutoML Methods
1 Hyperparameter Optimization............................................ 3
Matthias Feurer and Frank Hutter
2 Meta-Learning .............................................................. 35 Joaquin Vanschoren
3 Neural Architecture Search ............................................... 63 Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter
Part II AutoML Systems
4 Auto-WEKA: Automatic Model Selection and Hyperparameter Optimization in WEKA .................................................... 81 Lars Kotthoff, Chris Thornton, Holger H. Hoos, Frank Hutter,
and Kevin Leyton-Brown
5 Hyperopt-Sklearn........................................................... 97 Brent Komer, James Bergstra, and Chris Eliasmith
6 Auto-sklearn: Efficient and Robust Automated Machine
Learning ..................................................................... 113 Matthias Feurer, Aaron Klein, Katharina Eggensperger,
Jost Tobias Springenberg, Manuel Blum, and Frank Hutter
7 Towards Automatically-Tuned Deep Neural Networks................. 135 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 ....................................... 151 Randal S. Olson and Jason H. Moore
xiii
xiv Contents
9 The Automatic Statistician ................................................ 161 Christian Steinruecken, Emma Smith, David Janz, James Lloyd,
and Zoubin Ghahramani
Part III AutoML Challenges
10 Analysis of the AutoML Challenge Series 2015–2018 .................. 177 Isabelle Guyon, Lisheng Sun-Hosoya, Marc Boullé,
Hugo Jair Escalante, Sergio Escalera, Zhengying Liu, Damir Jajetic,
Bisakha Ray, Mehreen Saeed, Michèle Sebag, Alexander Statnikov,
Wei-Wei Tu, and Evelyne Viegas