mlsanityData to 14 Oct 2026

AutoML: A Survey of the State-of-the-Art

arXiv 1908.00709 ↗First posted 2 Aug 2019cs.LG cs.CV stat.MLcited by 390

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Cited by 22 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Deep learning (DL) techniques have penetrated all aspects of our lives and brought us great convenience. However, building a high-quality DL system for a specific task highly relies on human expertise, hindering the applications of DL to more areas. Automated machine learning (AutoML) becomes a promising solution to build a DL system without human assistance, and a growing number of researchers focus on AutoML. In this paper, we provide a comprehensive and up-to-date review of the state-of-the-art (SOTA) in AutoML. First, we introduce AutoML methods according to the pipeline, covering data preparation, feature engineering, hyperparameter optimization, and neural architecture search (NAS). We focus more on NAS, as it is currently very hot sub-topic of AutoML. We summarize the performance of the representative NAS algorithms on the CIFAR-10 and ImageNet datasets and further discuss several worthy studying directions of NAS methods: one/two-stage NAS, one-shot NAS, and joint hyperparameter and architecture optimization. Finally, we discuss some open problems of the existing AutoML methods for future research.

Key facts. Cited by 390 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Aug 2019.

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Citing papers per year: 2021: 61; 2022: 71; 2023: 81; 2024: 71; 2025: 63; 2026: 43.

390 papers.

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38 more
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  7. 2023Empirical Design in Reinforcement Learningcited by 55
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  11. 2023An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registrycited by 47
  12. 2022EEG based Emotion Recognition: A Tutorial and Reviewcited by 43
  13. 2025Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluationcited by 42
  14. 2021Automated Machine Learning on Graphs: A Surveycited by 39
  15. 2024Lightweight Deep Learning for Resource-Constrained Environments: A Surveycited by 38
  16. 2022PaSca: a Graph Neural Architecture Search System under the Scalable Paradigmcited by 37
  17. 2022Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overviewcited by 37
  18. 2023Self-Refined Large Language Model as Automated Reward Function Designer for Deep Reinforcement Learning in Roboticscited by 36
  19. 2025Representation Learning for Tabular Data: A Comprehensive Surveycited by 36
  20. 2021How to avoid machine learning pitfalls: a guide for academic researcherscited by 35
  21. 2025MAT-Agent: Adaptive Multi-Agent Training Optimizationcited by 35
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  23. 2022Enable Deep Learning on Mobile Devices: Methods, Systems, and Applicationscited by 32
  24. 2023OpenStereo: A Comprehensive Benchmark for Stereo Matching and Strong Baselinecited by 30
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  26. 2025AI Agentic Programming: A Survey of Techniques, Challenges, and Opportunitiescited by 28
  27. 2026MLEvolve: A Self-Evolving Framework for Automated Machine Learning Algorithm Discoverycited by 28
  28. 2024Heterogeneous Contrastive Learning for Foundation Models and Beyondcited by 27
  29. 2021Benchmarking Multimodal AutoML for Tabular Data with Text Fieldscited by 26
  30. 2022Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluationcited by 26
  31. 2021VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decompositioncited by 25
  32. 2021The Role of Adaptive Optimizers for Honest Private Hyperparameter Selectioncited by 25
  33. 2025ReviewEval: An Evaluation Framework for AI-Generated Reviewscited by 25
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  35. 2026AutoSOTA: An End-to-End Automated Research System for State-of-the-Art AI Model Discoverycited by 23
  36. 2024HyperFast: Instant Classification for Tabular Datacited by 22
  37. 2024Understanding the Importance of Evolutionary Search in Automated Heuristic Design with Large Language Modelscited by 22
  38. 2021Graph-based Deep Learning for Communication Networks: A Surveycited by 21

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