Techniques for Automated Machine Learning

2019/07/21 by Yiwei Chen, Chen, Yi-Wei, Qingquan Song +3 · 2 citations
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Imbalanced Data Classification Techniques #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Machine Learning and Data Classification

paper · pdf · doi:10.48550/arxiv.1907.08908

openalex publication_date 2019/07/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Automated machine learning (AutoML) aims to find optimal machine learning solutions automatically given a machine learning problem. It could release the burden of data scientists from the multifarious manual tuning process and enable the access of domain experts to the off-the-shelf machine learning solutions without extensive experience. In this paper, we review the current developments of AutoML in terms of three categories, automated feature engineering (AutoFE), automated model and hyperparameter learning (AutoMHL), and automated deep learning (AutoDL). State-of-the-art techniques adopted in the three categories are presented, including Bayesian optimization, reinforcement learning, evolutionary algorithm, and gradient-based approaches. We summarize popular AutoML frameworks and conclude with current open challenges of AutoML.

Citations

• Freeze-Thaw Bayesian Optimization
• Automated Machine Learning on Big Data using Stochastic Algorithm Tuning
• A few useful things to know about machine learning
• Neural Architecture Search with Reinforcement Learning
• DARTS: Differentiable Architecture Search
• Neural Architecture Optimization
• NAS-Bench-101: Towards Reproducible Neural Architecture Search
• Feature Engineering for Predictive Modeling using Reinforcement Learning
• Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
• ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware

Cited by

• GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement
• ELATE: Evolutionary Language model for Automated Time-series Engineering

Related

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• Automated Machine Learning -- a brief review at the end of the early years
• Automated Machine Learning: From Principles to Practices
• Automated Machine Learning: State-of-The-Art and Open Challenges
• AutoML to Date and Beyond: Challenges and Opportunities