mlsanityData to 7 Oct 2026

OpenML: networked science in machine learning

arXiv 1407.7722 ↗First posted 29 Jul 2014cs.LG cs.CYcited by 523

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Abstract (arXiv)

Many sciences have made significant breakthroughs by adopting online tools that help organize, structure and mine information that is too detailed to be printed in journals. In this paper, we introduce OpenML, a place for machine learning researchers to share and organize data in fine detail, so that they can work more effectively, be more visible, and collaborate with others to tackle harder problems. We discuss how OpenML relates to other examples of networked science and what benefits it brings for machine learning research, individual scientists, as well as students and practitioners.

Key facts. Cited by 523 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jul 2014.

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Citing papers per year: 2021: 73; 2022: 88; 2023: 87; 2024: 83; 2025: 86; 2026: 106.

523 papers.

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  1. 2021Revisiting Deep Learning Models for Tabular Datacited by 725
  2. 2022TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Secondcited by 643
  3. 2021Tabular Data: Deep Learning is Not All You Needcited by 530
  4. 2022TabDDPM: Modelling Tabular Data with Diffusion Modelscited by 373
  5. 2021SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Trainingcited by 310
  6. 2023When Do Neural Nets Outperform Boosted Trees on Tabular Data?cited by 218
  7. 2022Why do tree-based models still outperform deep learning on tabular data?cited by 198
  8. 2022On Embeddings for Numerical Features in Tabular Deep Learningcited by 185
  9. 2021Well-tuned Simple Nets Excel on Tabular Datasetscited by 151
  10. 2021SCARF: Self-Supervised Contrastive Learning using Random Feature Corruptioncited by 151
  11. 2022LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Taskscited by 146
  12. 2021Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challengescited by 143
38 more
  1. 2023Looped Transformers are Better at Learning Learning Algorithmscited by 135
  2. 2023Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineeringcited by 125
  3. 2021HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPOcited by 111
  4. 2022Interpretable Scientific Discovery with Symbolic Regression: A Reviewcited by 105
  5. 2024TabDPT: Scaling Tabular Foundation Models on Real Datacited by 97
  6. 2022Towards Learning Universal Hyperparameter Optimizers with Transformerscited by 81
  7. 2024Croissant: A Metadata Format for ML-Ready Datasetscited by 79
  8. 2021HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenMLcited by 75
  9. 2022πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimizationcited by 69
  10. 2021Scikit-dimension: a Python package for intrinsic dimension estimationcited by 68
  11. 2023MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Taskscited by 66
  12. 2021Codabench: Flexible, Easy-to-Use and Reproducible Benchmarking Platformcited by 65
  13. 2021YAHPO Gym -- An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimizationcited by 61
  14. 2023TorchRL: A data-driven decision-making library for PyTorchcited by 59
  15. 2022Joint Entropy Search for Maximally-Informed Bayesian Optimizationcited by 55
  16. 2022AMLB: an AutoML Benchmarkcited by 55
  17. 2023Generative Table Pre-training Empowers Models for Tabular Predictioncited by 55
  18. 2023OpenDataVal: a Unified Benchmark for Data Valuationcited by 55
  19. 2021Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discriminationcited by 53
  20. 2023Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimescited by 52
  21. 2023STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tablescited by 48
  22. 2022A Framework and Benchmark for Deep Batch Active Learning for Regressioncited by 47
  23. 2024TuneTables: Context Optimization for Scalable Prior-Data Fitted Networkscited by 47
  24. 2022Revisiting Pretraining Objectives for Tabular Deep Learningcited by 43
  25. 2021LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lassocited by 41
  26. 2021Off-Policy Evaluation via Adaptive Weighting with Data from Contextual Banditscited by 40
  27. 2021On Efficiently Explaining Graph-Based Classifierscited by 37
  28. 2023Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networkscited by 37
  29. 2023Trompt: Towards a Better Deep Neural Network for Tabular Datacited by 36
  30. 2025Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Datacited by 36
  31. 2021Explaining Hyperparameter Optimization via Partial Dependence Plotscited by 35
  32. 2025LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizerscited by 35
  33. 2023Cross-Modal Fine-Tuning: Align then Refinecited by 34
  34. 2024Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language Modelscited by 34
  35. 2023TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applicationscited by 32
  36. 2021RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learningcited by 31
  37. 2022Randomly pivoted Cholesky: Practical approximation of a kernel matrix with few entry evaluationscited by 31
  38. 2023Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classificationcited by 29

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