Automated Machine Learning: From Principles to Practices

TL;DR

This survey comprehensively reviews AutoML, covering its principles, taxonomy, applications, and emerging directions, highlighting its role in automating ML configuration and selection.

Contribution

It provides a formal definition, principles, taxonomy, and practical applications of AutoML, offering a unified overview of the field and its recent advancements.

Findings

AutoML encompasses various search spaces, algorithms, and evaluation strategies.

AutoML applications include pipeline configuration, neural architecture search, and foundation model integration.

Emerging directions focus on scalability, efficiency, and broader applicability.

Abstract

Machine learning (ML) methods have been developing rapidly, but configuring and selecting proper methods to achieve a desired performance is increasingly difficult and tedious. To address this challenge, automated machine learning (AutoML) has emerged, which aims to generate satisfactory ML configurations for given tasks in a data-driven way. In this paper, we provide a comprehensive survey on this topic. We begin with the formal definition of AutoML and then introduce its principles, including the bi-level learning objective, the learning strategy, and the theoretical interpretation. Then, we summarize the AutoML practices by setting up the taxonomy of existing works based on three main factors: the search space, the search algorithm, and the evaluation strategy. Each category is also explained with the representative methods. Then, we illustrate the principles and practices with exemplary applications from configuring ML pipeline, one-shot neural architecture search, and integration with foundation models. Finally, we highlight the emerging directions of AutoML and conclude the survey.