Taking Human out of Learning Applications: A Survey on Automated Machine Learning

TLDR

Machine learning is ubiquitous, yet its success depends on labor‑intensive human expertise, prompting the rise of automated machine learning to lower the barrier to entry. This paper surveys the current state of AutoML, offering an up‑to‑date overview of the field. The authors define the AutoML problem, present a comprehensive framework that encompasses existing methods, and systematically review and analyze works by problem setup and techniques to explain their effectiveness. The survey aims to guide newcomers and stimulate future research in AutoML.

Abstract

Machine learning techniques have deeply rooted in our everyday life. However, since it is knowledge- and labor-intensive to pursue good learning performance, human experts are heavily involved in every aspect of machine learning. In order to make machine learning techniques easier to apply and reduce the demand for experienced human experts, automated machine learning (AutoML) has emerged as a hot topic with both industrial and academic interest. In this paper, we provide an up to date survey on AutoML. First, we introduce and define the AutoML problem, with inspiration from both realms of automation and machine learning. Then, we propose a general AutoML framework that not only covers most existing approaches to date but also can guide the design for new methods. Subsequently, we categorize and review the existing works from two aspects, i.e., the problem setup and the employed techniques. Finally, we provide a detailed analysis of AutoML approaches and explain the reasons underneath their successful applications. We hope this survey can serve as not only an insightful guideline for AutoML beginners but also an inspiration for future research.