# Automated Machine Learning -- a brief review at the end of the early years

arXiv: https://arxiv.org/abs/2008.08516 · HTML: https://mlsanity.com/paper/2008.08516/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2020.

## Built on by (most cited first, 9 of 9)

- [Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML](https://mlsanity.com/paper/2303.08485/) (2023)
- [Position: A Call to Action for a Human-Centered AutoML Paradigm](https://mlsanity.com/paper/2406.03348/) (2024)
- [Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis](https://mlsanity.com/paper/2502.21286/) (2025)
- [Machine Learning Systems: A Survey from a Data-Oriented Perspective](https://mlsanity.com/paper/2302.04810/) (2023)
- [Multimodal Representation Learning and Fusion](https://mlsanity.com/paper/2506.20494/) (2025)
- [A General Recipe for Automated Machine Learning in Practice](https://mlsanity.com/paper/2308.15647/) (2023)
- [GenAutoML: An Agentic Framework for Dynamic Architecture Generation and Optimization in Time-Series Analysis](https://mlsanity.com/paper/2606.05860/) (2026)
- [Deciphering AutoML Ensembles: cattleia's Assistance in Decision-Making](https://mlsanity.com/paper/2403.12664/) (2024)
- [NiaAutoARM: Automated generation and evaluation of Association Rule Mining pipelines](https://mlsanity.com/paper/2501.00138/) (2024)

## Abstract (arXiv)

Automated machine learning (AutoML) is the sub-field of machine learning that aims at automating, to some extend, all stages of the design of a machine learning system. In the context of supervised learning, AutoML is concerned with feature extraction, pre processing, model design and post processing. Major contributions and achievements in AutoML have been taking place during the recent decade. We are therefore in perfect timing to look back and realize what we have learned. This chapter aims to summarize the main findings in the early years of AutoML. More specifically, in this chapter an introduction to AutoML for supervised learning is provided and an historical review of progress in this field is presented. Likewise, the main paradigms of AutoML are described and research opportunities are outlined.