mlsanityData to 7 Oct 2026

Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning

arXiv 2007.04074 ↗First posted 8 Jul 2020cs.LG stat.MLcited by 174

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Cited by 21 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Automated Machine Learning (AutoML) supports practitioners and researchers with the tedious task of designing machine learning pipelines and has recently achieved substantial success. In this paper, we introduce new AutoML approaches motivated by our winning submission to the second ChaLearn AutoML challenge. We develop PoSH Auto-sklearn, which enables AutoML systems to work well on large datasets under rigid time limits by using a new, simple and meta-feature-free meta-learning technique and by employing a successful bandit strategy for budget allocation. However, PoSH Auto-sklearn introduces even more ways of running AutoML and might make it harder for users to set it up correctly. Therefore, we also go one step further and study the design space of AutoML itself, proposing a solution towards truly hands-free AutoML. Together, these changes give rise to the next generation of our AutoML system, Auto-sklearn 2.0. We verify the improvements by these additions in an extensive experimental study on 39 AutoML benchmark datasets. We conclude the paper by comparing to other popular AutoML frameworks and Auto-sklearn 1.0, reducing the relative error by up to a factor of 4.5, and yielding a performance in 10 minutes that is substantially better than what Auto-sklearn 1.0 achieves within an hour.

Key facts. Cited by 174 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jul 2020.

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Citing papers per year: 2021: 4; 2022: 25; 2023: 28; 2024: 36; 2025: 49; 2026: 32.

174 papers.

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  11. 2025MLE-STAR: Machine Learning Engineering Agent via Search and Targeted Refinementcited by 52
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38 more
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  2. 2023AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Riskscited by 34
  3. 2023TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applicationscited by 32
  4. 2023PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learningcited by 31
  5. 2024AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Modelscited by 29
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  7. 2021Towards Green Automated Machine Learning: Status Quo and Future Directionscited by 25
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  9. 2024SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learningcited by 23
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  11. 2026MLEvolve: A Self-Evolving Framework for Automated Machine Learning Algorithm Discoverycited by 20
  12. 2022AutoML Two-Sample Testcited by 19
  13. 2022The Missing Indicator Method: From Low to High Dimensionscited by 19
  14. 2024Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Surveycited by 19
  15. 2025AutoMLGen: Navigating Fine-Grained Optimization for Coding Agentscited by 17
  16. 2023Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoMLcited by 16
  17. 2021Fast and Informative Model Selection using Learning Curve Cross-Validationcited by 15
  18. 2025TabSTAR: A Tabular Foundation Model for Tabular Data with Text Fieldscited by 15
  19. 2024Position: A Call to Action for a Human-Centered AutoML Paradigmcited by 14
  20. 2025TabFlex: Scaling Tabular Learning to Millions with Linear Attentioncited by 13
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  23. 2025Empowering Time Series Forecasting with LLM-Agentscited by 9
  24. 2022SubStrat: A Subset-Based Strategy for Faster AutoMLcited by 8
  25. 2023Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoMLcited by 8
  26. 2023DiffPrep: Differentiable Data Preprocessing Pipeline Search for Learning over Tabular Datacited by 8
  27. 2025Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformercited by 8
  28. 2022Hyperparameter Importance of Quantum Neural Networks Across Small Datasetscited by 7
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  30. 2023On Leakage in Machine Learning Pipelinescited by 7
  31. 2024Reshuffling Resampling Splits Can Improve Generalization of Hyperparameter Optimizationcited by 7
  32. 2025Prior-Fitted Networks Scale to Larger Datasets When Treated as Weak Learnerscited by 7
  33. 2025SPIO: Ensemble and Selective Strategies via LLM-Based Multi-Agent Planning in Automated Data Sciencecited by 7
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  36. 2025KompeteAI: Accelerated Autonomous Multi-Agent System for End-to-End Pipeline Generation for Machine Learning Problemscited by 7
  37. 2025What Does It Take to Be a Good AI Research Agent? Studying the Role of Ideation Diversitycited by 7
  38. 2023AutoXPCR: Automated Multi-Objective Model Selection for Time Series Forecastingcited by 6

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