# Automated Machine Learning: From Principles to Practices

arXiv: https://arxiv.org/abs/1810.13306 · HTML: https://mlsanity.com/paper/1810.13306/

## Key facts

- Cited by 62 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2018.

## Built on by (most cited first, 30 of 62)

- [Concealed Object Detection](https://mlsanity.com/paper/2102.10274/) (2021)
- [Learning to Optimize: A Primer and A Benchmark](https://mlsanity.com/paper/2103.12828/) (2021)
- [Neural Architecture Search: Insights from 1000 Papers](https://mlsanity.com/paper/2301.08727/) (2023)
- [From Automation to Autonomy: A Survey on Large Language Models in Scientific Discovery](https://mlsanity.com/paper/2505.13259/) (2025)
- [DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization](https://mlsanity.com/paper/2204.06504/) (2022)
- [AutoField: Automating Feature Selection in Deep Recommender Systems](https://mlsanity.com/paper/2204.09078/) (2022)
- [Historical Inertia: A Neglected but Powerful Baseline for Long Sequence Time-series Forecasting](https://mlsanity.com/paper/2103.16349/) (2021)
- [VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition](https://mlsanity.com/paper/2107.08861/) (2021)
- [Fits and Starts: Enterprise Use of AutoML and the Role of Humans in the Loop](https://mlsanity.com/paper/2101.04296/) (2021)
- [AutoGL: A Library for Automated Graph Learning](https://mlsanity.com/paper/2104.04987/) (2021)
- [Searching to Sparsify Tensor Decomposition for N-ary Relational Data](https://mlsanity.com/paper/2104.10625/) (2021)
- [Hierarchical Heterogeneous Graph Representation Learning for Short Text Classification](https://mlsanity.com/paper/2111.00180/) (2021)
- [Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale](https://mlsanity.com/paper/2201.06834/) (2022)
- [Bilinear Scoring Function Search for Knowledge Graph Learning](https://mlsanity.com/paper/2107.00184/) (2021)
- [AutoLossGen: Automatic Loss Function Generation for Recommender Systems](https://mlsanity.com/paper/2204.13160/) (2022)
- [Learning an Explicit Hyperparameter Prediction Function Conditioned on Tasks](https://mlsanity.com/paper/2107.02378/) (2021)
- [Recommender systems based on graph embedding techniques: A comprehensive review](https://mlsanity.com/paper/2109.09587/) (2021)
- [Neural Chain-of-Thought Search: Searching the Optimal Reasoning Path to Enhance Large Language Models](https://mlsanity.com/paper/2601.11340/) (2026)
- [On the Opportunities of Green Computing: A Survey](https://mlsanity.com/paper/2311.00447/) (2023)
- [SPIO: Ensemble and Selective Strategies via LLM-Based Multi-Agent Planning in Automated Data Science](https://mlsanity.com/paper/2503.23314/) (2025)
- [DivBO: Diversity-aware CASH for Ensemble Learning](https://mlsanity.com/paper/2302.03255/) (2023)
- [CONetV2: Efficient Auto-Channel Size Optimization for CNNs](https://mlsanity.com/paper/2110.06830/) (2021)
- [The Roles and Modes of Human Interactions with Automated Machine Learning Systems](https://mlsanity.com/paper/2205.04139/) (2022)
- [An Empirical Study on the Usage of Automated Machine Learning Tools](https://mlsanity.com/paper/2208.13116/) (2022)
- [Survey of Graph Neural Network for Internet of Things and NextG Networks](https://mlsanity.com/paper/2405.17309/) (2024)
- [Customized Subgraph Selection and Encoding for Drug-drug Interaction Prediction](https://mlsanity.com/paper/2411.01535/) (2024)
- [Efficient Data-specific Model Search for Collaborative Filtering](https://mlsanity.com/paper/2106.07453/) (2021)
- [Self-directed Machine Learning](https://mlsanity.com/paper/2201.01289/) (2022)
- [Semantic-Based Neural Network Repair](https://mlsanity.com/paper/2306.07995/) (2023)
- [Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering](https://mlsanity.com/paper/2307.11004/) (2023)

## Abstract (arXiv)

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.