mlsanityData to 7 Oct 2026

AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data

arXiv 2003.06505 ↗First posted 13 Mar 2020stat.ML cs.LGcited by 368

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Cited by 29 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We introduce AutoGluon-Tabular, an open-source AutoML framework that requires only a single line of Python to train highly accurate machine learning models on an unprocessed tabular dataset such as a CSV file. Unlike existing AutoML frameworks that primarily focus on model/hyperparameter selection, AutoGluon-Tabular succeeds by ensembling multiple models and stacking them in multiple layers. Experiments reveal that our multi-layer combination of many models offers better use of allocated training time than seeking out the best. A second contribution is an extensive evaluation of public and commercial AutoML platforms including TPOT, H2O, AutoWEKA, auto-sklearn, AutoGluon, and Google AutoML Tables. Tests on a suite of 50 classification and regression tasks from Kaggle and the OpenML AutoML Benchmark reveal that AutoGluon is faster, more robust, and much more accurate. We find that AutoGluon often even outperforms the best-in-hindsight combination of all of its competitors. In two popular Kaggle competitions, AutoGluon beat 99% of the participating data scientists after merely 4h of training on the raw data.

Key facts. Cited by 368 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Mar 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 31; 2022: 33; 2023: 52; 2024: 68; 2025: 80; 2026: 104.

368 papers.

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  1. 2022TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Secondcited by 643
  2. 2021SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Trainingcited by 310
  3. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)cited by 170
  4. 2021Well-tuned Simple Nets Excel on Tabular Datasetscited by 151
  5. 2021Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challengescited by 143
  6. 2023Neural Architecture Search: Insights from 1000 Paperscited by 136
  7. 2025Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systemscited by 125
  8. 2024DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoningcited by 119
  9. 2025TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Modelscited by 115
  10. 2023Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlookcited by 109
  11. 2023ForecastPFN: Synthetically-Trained Zero-Shot Forecastingcited by 102
  12. 2024AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoMLcited by 84
38 more
  1. 2023XTab: Cross-table Pretraining for Tabular Transformerscited by 81
  2. 2021MMD Aggregated Two-Sample Testcited by 74
  3. 2024DrivAerNet++: A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarkscited by 74
  4. 2025Mitra: Mixed Synthetic Priors for Enhancing Tabular Foundation Modelscited by 67
  5. 2025ML-Master: Towards AI-for-AI via Integration of Exploration and Reasoningcited by 57
  6. 2022AMLB: an AutoML Benchmarkcited by 55
  7. 2022OpenFE: Automated Feature Generation with Expert-level Performancecited by 52
  8. 2025MLE-STAR: Machine Learning Engineering Agent via Search and Targeted Refinementcited by 52
  9. 2021Automated Machine Learning on Graphs: A Surveycited by 38
  10. 2023AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecastingcited by 36
  11. 2025Representation Learning for Tabular Data: A Comprehensive Surveycited by 36
  12. 2023Cross-Modal Fine-Tuning: Align then Refinecited by 34
  13. 2023AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Riskscited by 34
  14. 2025R&D-Agent: An LLM-Agent Framework Towards Autonomous Data Sciencecited by 33
  15. 2021LightAutoML: AutoML Solution for a Large Financial Services Ecosystemcited by 32
  16. 2023TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applicationscited by 32
  17. 2024How Far Are We From AGI: Are LLMs All We Need?cited by 31
  18. 2025ConTextTab: A Semantics-Aware Tabular In-Context Learnercited by 31
  19. 2024Applied Causal Inference Powered by ML and AIcited by 30
  20. 2024AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Modelscited by 29
  21. 2023Transfer Learning for Bayesian Optimization: A Surveycited by 27
  22. 2024Differentiable Weightless Neural Networkscited by 27
  23. 2025ML-Agent: Reinforcing LLM Agents for Autonomous Machine Learning Engineeringcited by 26
  24. 2021Benchmarking Multimodal AutoML for Tabular Data with Text Fieldscited by 24
  25. 2024SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learningcited by 23
  26. 2021Flexible Model Aggregation for Quantile Regressioncited by 22
  27. 2021Multi-objective Asynchronous Successive Halvingcited by 22
  28. 2024HyperFast: Instant Classification for Tabular Datacited by 22
  29. 2025MLZero: A Multi-Agent System for End-to-end Machine Learning Automationcited by 22
  30. 2021PyGlove: Symbolic Programming for Automated Machine Learningcited by 19
  31. 2022AutoML Two-Sample Testcited by 19
  32. 2022Hopular: Modern Hopfield Networks for Tabular Datacited by 18
  33. 2022Back to the Basics: Revisiting Out-of-Distribution Detection Baselinescited by 16
  34. 2022CROWDLAB: Supervised learning to infer consensus labels and quality scores for data with multiple annotatorscited by 16
  35. 2023Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoMLcited by 16
  36. 2024A Survey on Self-Supervised Learning for Non-Sequential Tabular Datacited by 15
  37. 2024A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Datacited by 15
  38. 2025TabSTAR: A Tabular Foundation Model for Tabular Data with Text Fieldscited by 15

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