mlsanityData to 14 Oct 2026

TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications

arXiv 2311.02971 ↗First posted 6 Nov 2023cs.LG cs.AIcited by 33

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Abstract (arXiv)

We introduce TabRepo, a new dataset of tabular model evaluations and predictions. TabRepo contains the predictions and metrics of 1310 models evaluated on 200 classification and regression datasets. We illustrate the benefit of our dataset in multiple ways. First, we show that it allows to perform analysis such as comparing Hyperparameter Optimization against current AutoML systems while also considering ensembling at marginal cost by using precomputed model predictions. Second, we show that our dataset can be readily leveraged to perform transfer-learning. In particular, we show that applying standard transfer-learning techniques allows to outperform current state-of-the-art tabular systems in accuracy, runtime and latency.

Key facts. Cited by 33 papers in the map (data through 14 Oct 2026). Builds on 29 of its 58 references that are in the map. First posted on arXiv 6 Nov 2023.

Builds on

29 of 58 references.

References · 29

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  2. 2021Revisiting Deep Learning Models for Tabular Datacited by 740
  3. 2022TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Secondcited by 674
  4. 2014OpenML: networked science in machine learningcited by 526
  5. 2020NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Searchcited by 416
  6. 2019AutoML: A Survey of the State-of-the-Artcited by 390
  7. 2020AutoGluon-Tabular: Robust and Accurate AutoML for Structured Datacited by 383
  8. 2019NAS-Bench-101: Towards Reproducible Neural Architecture Searchcited by 344
  9. 2021Multimodal datasets: misogyny, pornography, and malignant stereotypescited by 327
  10. 2023When Do Neural Nets Outperform Boosted Trees on Tabular Data?cited by 223
  11. 2022Why do tree-based models still outperform deep learning on tabular data?cited by 199
  12. 2020Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learningcited by 174
17 more
  1. 2021HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPOcited by 112
  2. 2019FLAML: A Fast and Lightweight AutoML Librarycited by 106
  3. 2019An Open Source AutoML Benchmarkcited by 85
  4. 2023XTab: Cross-table Pretraining for Tabular Transformerscited by 81
  5. 2023TabR: Tabular Deep Learning Meets Nearest Neighbors in 2023cited by 74
  6. 2019NAS evaluation is frustratingly hardcited by 70
  7. 2020Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDLcited by 67
  8. 2019Tabular Benchmarks for Joint Architecture and Hyperparameter Optimizationcited by 58
  9. 2022AMLB: an AutoML Benchmarkcited by 55
  10. 2019Dataset2Vec: Learning Dataset Meta-Featurescited by 38
  11. 2021LightAutoML: AutoML Solution for a Large Financial Services Ecosystemcited by 32
  12. 2023Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and Howcited by 18
  13. 2020Practical and sample efficient zero-shot HPOcited by 9
  14. 2023Assembled-OpenML: Creating Efficient Benchmarks for Ensembles in AutoML with OpenMLcited by 9
  15. 2021Naive Automated Machine Learningcited by 8
  16. 2022Multi-Objective Model Selection for Time Series Forecastingcited by 8
  17. 2023CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failurecited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 9; 2025: 11; 2026: 13.

33 papers.

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  1. 2025Mitra: Mixed Synthetic Priors for Enhancing Tabular Foundation Modelscited by 77
  2. 2025MLZero: A Multi-Agent System for End-to-end Machine Learning Automationcited by 24
  3. 2024HW-GPT-Bench: Hardware-Aware Architecture Benchmark for Language Modelscited by 10
  4. 2025Structured Matrix Scaling for Multi-Class Calibrationcited by 9
  5. 2026From Zero to Hero: Advancing Zero-Shot Foundation Models for Tabular Outlier Detectioncited by 9
  6. 2024Don't Waste Your Time: Early Stopping Cross-Validationcited by 6
  7. 2024PMLBmini: A Tabular Classification Benchmark Suite for Data-Scarce Applicationscited by 6
  8. 2024"Oh LLM, I'm Asking Thee, Please Give Me a Decision Tree": Zero-Shot Decision Tree Induction and Embedding with Large Language Modelscited by 6
  9. 2024Transfer Learning for Finetuning Large Language Modelscited by 5
  10. 2025Overtuning in Hyperparameter Optimizationcited by 5
  11. 2026MacrOData: New Benchmarks of Thousands of Datasets for Tabular Outlier Detectioncited by 5
  12. 2025Put CASH on Bandits: A Max K-Armed Problem for Automated Machine Learningcited by 4
21 more
  1. 2024Ensembling Finetuned Language Models for Text Classificationcited by 2
  2. 2025MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domainscited by 2
  3. 2026CalArena: A Large-Scale Post-Hoc Calibration Benchmarkcited by 2
  4. 2024Squeezing Lemons with Hammers: An Evaluation of AutoML and Tabular Deep Learning for Data-Scarce Classification Applicationscited by 1
  5. 2024Regularized Neural Ensemblerscited by 1
  6. 2025AutoML Benchmark with shorter time constraints and early stoppingcited by 1
  7. 2025LightAutoDS-Tab: Multi-AutoML Agentic System for Tabular Datacited by 1
  8. 2026HAPEns: Hardware-Aware Post-Hoc Ensembling for Tabular Datacited by 1
  9. 2026LUCoS: Latent Unsupervised Context Selection for Tabular Foundation Modelscited by 1
  10. 2024Hardware Aware Ensemble Selection for Balancing Predictive Accuracy and Costcited by 0
  11. 2025In-Context Decision Making for Optimizing Complex AutoML Pipelinescited by 0
  12. 2025Ensemble of Pre-Trained Models for Long-Tailed Trajectory Predictioncited by 0
  13. 2025Multi-layer Stack Ensembles for Time Series Forecastingcited by 0
  14. 2026Unsupervised Ensemble Learning Through Deep Energy-based Modelscited by 0
  15. 2026V-ECE: Estimating General Expected Calibration Errorscited by 0
  16. 2026An Open-Source Training Dataset for Foundation Models for Black-box Optimizationcited by 0
  17. 2026BBOmix: A Tabular Benchmark for Hyperparameter Optimization of Unsupervised Biological Representation Learningcited by 0
  18. 2026Partition Scores Are Not System Scores: Deployment-Fidelity Gaps in Decomposed Algorithm Selectioncited by 0
  19. 2026Agentic Search Spaces for Tabular Machine Learningcited by 0
  20. 2026Test-Time Compute for Tabular Foundation Models: Mechanisms, Gains, and Limitscited by 0
  21. 2026TabMF: Learning Dataset Representations with Tabular Foundation Modelscited by 0