Editors: Frank Hutter, Lars Kotthoff, Joaquin Vanschoren
We’re in the process of finishing this edited book, and it should be ready for display at NIPS 2018. Next to publishing, we will keep the book open access.
Here is a draft PDF of the entire book, and a bibtex entry for it. Below, we share preliminary versions of the chapters; at this point in time, these are all drafts, before copy editing.
Part 1: AutoML Methods
This part comprises highly up-to-date overview chapters on the common foundations behind all AutoML systems.
Part 2: AutoML Systems
This part comprises in-depth descriptions of a broad range of available AutoML systems that can be used for effective machine learning out of the box.
Chapter 6: Auto-sklearn: Efficient and Robust Automated Machine Learning [bibtex]. By Matthias Feurer and Aaron Klein and Katharina Eggensperger and Jost Tobias Springenberg and Manuel Blum and Frank Hutter
Chapter 7: Auto-Net: Towards Automatically-Tuned Neural Networks [bibtex]. By Hector Mendoza and Aaron Klein and Matthias Feurer and Jost Tobias Springenberg and Matthias Urban and Michael Burkart and Max Dippel and Marius Lindauer and Frank Hutter
Chapter 8: TPOT: A Tool for Automating Machine Learning [bibtex]. By Randal S. Olson and Jason H. Moore
Part 3: AutoML Challenges
This part provides an in-depth analysis of all AutoML challenges held to date.
Chapter 10: Analysis of the AutoML Challenge series 2015-2018 [bibtex] [online appendix]. By Isabelle Guyon and Lisheng Sun-Hosoya and Marc Boull ́e and Hugo Jair Escalante and Sergio Escalera and Zhengying Liu and Damir Jajetic and Bisakha Ray and Mehreen Saeed and Michele Sebag and Alexander Statnikov and Wei-Wei Tu and Evelyne Viegas