Automated Machine Learning: Methods, Systems, Challenges

By Frank Hutter, Lars Kotthoff, Joaquin Vanschoren. The Springer Series on Challenges in Machine Learning, 2019

565 citations recorded by the source corpus.

This open access book presents the first comprehensive overview of general methods in Automatic Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first international challenge of AutoML systems. The book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. Many of the recent machine learning successes crucially rely on human experts, who select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters; however the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/598563b8320e6760b828cf08e2adc41c7027d0fe

Citations in the corpus

• Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges (2021, unknown): “Feurer and Hutter ( 2019) offered a thorough overview about existing HPO approaches, open challenges, and future research directions.”