TabArena: A Living Benchmark for Machine Learning on Tabular Data

• URL: http://arxiv.org/abs/2506.16791v4
• Date: Mon, 03 Nov 2025 18:47:03 GMT
• Title: TabArena: A Living Benchmark for Machine Learning on Tabular Data
• Authors: Nick Erickson, Lennart Purucker, Andrej Tschalzev, David Holzmüller, Prateek Mutalik Desai, David Salinas, Frank Hutter,
• Abstract summary: We introduce TabArena, the first continuously maintained living benchmarking system.<n>We manually curate a representative collection of datasets and well-implemented models.<n>We show that deep learning methods have caught up under larger time budgets with ensembling.<n>We observe that some deep learning models are overrepresented in cross-model ensembles due to validation set overfitting.
• Score: 45.52876263971067
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the growing popularity of deep learning and foundation models for tabular data, the need for standardized and reliable benchmarks is higher than ever. However, current benchmarks are static. Their design is not updated even if flaws are discovered, model versions are updated, or new models are released. To address this, we introduce TabArena, the first continuously maintained living tabular benchmarking system. To launch TabArena, we manually curate a representative collection of datasets and well-implemented models, conduct a large-scale benchmarking study to initialize a public leaderboard, and assemble a team of experienced maintainers. Our results highlight the influence of validation method and ensembling of hyperparameter configurations to benchmark models at their full potential. While gradient-boosted trees are still strong contenders on practical tabular datasets, we observe that deep learning methods have caught up under larger time budgets with ensembling. At the same time, foundation models excel on smaller datasets. Finally, we show that ensembles across models advance the state-of-the-art in tabular machine learning. We observe that some deep learning models are overrepresented in cross-model ensembles due to validation set overfitting, and we encourage model developers to address this issue. We launch TabArena with a public leaderboard, reproducible code, and maintenance protocols to create a living benchmark available at https://tabarena.ai.

Related papers

• End-to-End Compression for Tabular Foundation Models [20.50130399990578]
  We propose TACO, an end-to-end compression model that compresses the training dataset in a latent space.<n>We test our method on the TabArena benchmark, where our proposed method is up to 94x faster in inference time, while consuming up to 97% less memory.
  arXiv  Detail & Related papers  (2026-02-05T13:33:58Z)
• Comparing Task-Agnostic Embedding Models for Tabular Data [1.6479389738270018]
  This work specifically focuses on representation learning, i.e., on transferable, task-agnostic embeddings.<n>Tableizer features achieve comparable or superior performance while being up to three orders of magnitude faster than recent foundation models.
  arXiv  Detail & Related papers  (2025-11-18T09:10:40Z)
• nanoTabPFN: A Lightweight and Educational Reimplementation of TabPFN [78.62756717376563]
  nanoTabPFN is a lightweight implementation of the TabPFN v2 architecture and a corresponding training loop.<n>It achieves a performance comparable to traditional machine learning baselines within one minute of pre-training on a single GPU.
  arXiv  Detail & Related papers  (2025-11-05T16:52:51Z)
• Estimating Time Series Foundation Model Transferability via In-Context Learning [74.65355820906355]
  Time series foundation models (TSFMs) offer strong zero-shot forecasting via large-scale pre-training.<n>Fine-tuning remains critical for boosting performance in domains with limited public data.<n>We introduce TimeTic, a transferability estimation framework that recasts model selection as an in-context-learning problem.
  arXiv  Detail & Related papers  (2025-09-28T07:07:13Z)
• TabSTAR: A Foundation Tabular Model With Semantically Target-Aware Representations [14.12892960275563]
  Tabular Foundation Models can leverage real-world knowledge and generalize across diverse datasets.<n>We introduce TabSTAR: a Foundation Tabular Model with Semantically Target-Aware Representations.
  arXiv  Detail & Related papers  (2025-05-23T17:34:28Z)
• Representation Learning for Tabular Data: A Comprehensive Survey [23.606506938919605]
  Tabular data, structured as rows and columns, is among the most prevalent data types in machine learning classification and regression applications.<n>Deep Neural Networks (DNNs) have recently demonstrated promising results through their capability of representation learning.<n>We organize existing methods into three main categories according to their generalization capabilities.
  arXiv  Detail & Related papers  (2025-04-17T17:58:23Z)
• TabDPT: Scaling Tabular Foundation Models on Real Data [20.00390825519329]
  We propose an approach to combine ICL-based retrieval with self supervised learning to train foundation models.<n>We show that incorporating real data during the pre-training phase can lead to significantly faster training and better generalization to unseen data.<n>Our resulting model, TabDPT, achieves top performance on both regression (CTR23) and classification (CC18) benchmarks.
  arXiv  Detail & Related papers  (2024-10-23T18:00:00Z)
• TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks [30.922069185335246]
  We find two common characteristics of tabular data in typical industrial applications that are underrepresented in the datasets usually used for evaluation in the literature. A considerable portion of datasets in production settings stem from extensive data acquisition and feature engineering pipelines. This can have an impact on the absolute and relative number of predictive, uninformative, and correlated features compared to academic datasets.
  arXiv  Detail & Related papers  (2024-06-27T17:55:31Z)
• LaTable: Towards Large Tabular Models [63.995130144110156]
  Tabular generative foundation models are hard to build due to the heterogeneous feature spaces of different datasets. LaTable is a novel diffusion model that addresses these challenges and can be trained across different datasets. We find that LaTable outperforms baselines on in-distribution generation, and that finetuning LaTable can generate out-of-distribution datasets better with fewer samples.
  arXiv  Detail & Related papers  (2024-06-25T16:03:50Z)
• Universal Domain Adaptation from Foundation Models: A Baseline Study [58.51162198585434]
  We make empirical studies of state-of-the-art UniDA methods using foundation models. We introduce textitCLIP distillation, a parameter-free method specifically designed to distill target knowledge from CLIP models. Although simple, our method outperforms previous approaches in most benchmark tasks.
  arXiv  Detail & Related papers  (2023-05-18T16:28:29Z)
• Generative Table Pre-training Empowers Models for Tabular Prediction [71.76829961276032]
  We propose TapTap, the first attempt that leverages table pre-training to empower models for tabular prediction. TapTap can generate high-quality synthetic tables to support various applications, including privacy protection, low resource regime, missing value imputation, and imbalanced classification. It can be easily combined with various backbone models, including LightGBM, Multilayer Perceptron (MLP) and Transformer.
  arXiv  Detail & Related papers  (2023-05-16T06:37:38Z)
• STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tables [64.0903766169603]
  We propose a framework for few-shot semi-supervised learning, coined Self-generated Tasks from UNlabeled Tables (STUNT) Our key idea is to self-generate diverse few-shot tasks by treating randomly chosen columns as a target label. We then employ a meta-learning scheme to learn generalizable knowledge with the constructed tasks.
  arXiv  Detail & Related papers  (2023-03-02T02:37:54Z)
• Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning [92.89846887298852]
  Consider making a prediction over new test data without any opportunity to learn from a training set of labelled data. Give access to a set of expert models and their predictions alongside some limited information about the dataset used to train them.
  arXiv  Detail & Related papers  (2022-10-11T10:20:31Z)
• Why do tree-based models still outperform deep learning on tabular data? [0.0]
  We show that tree-based models remain state-of-the-art on medium-sized data. We conduct an empirical investigation into the differing inductive biases of tree-based models and Neural Networks (NNs)
  arXiv  Detail & Related papers  (2022-07-18T08:36:08Z)
• SLADE: A Self-Training Framework For Distance Metric Learning [75.54078592084217]
  We present a self-training framework, SLADE, to improve retrieval performance by leveraging additional unlabeled data. We first train a teacher model on the labeled data and use it to generate pseudo labels for the unlabeled data. We then train a student model on both labels and pseudo labels to generate final feature embeddings.
  arXiv  Detail & Related papers  (2020-11-20T08:26:10Z)
• BREEDS: Benchmarks for Subpopulation Shift [98.90314444545204]
  We develop a methodology for assessing the robustness of models to subpopulation shift. We leverage the class structure underlying existing datasets to control the data subpopulations that comprise the training and test distributions. Applying this methodology to the ImageNet dataset, we create a suite of subpopulation shift benchmarks of varying granularity.
  arXiv  Detail & Related papers  (2020-08-11T17:04:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.