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Methods › General › Deep Tabular Learning › TABPFN

tabular data Prior-data Fitted Network

TABPFN

26 papers tagged archive 2025-07-28

Introduced by Noah Hollmann et al. in TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

We present TabPFN, a trained Transformer that can do supervised classification for small tabular datasets in less than a second, needs no hyperparameter tuning and is competitive with state-of-the-art classification methods. TabPFN is fully entailed in the weights of our network, which accepts training and test samples as a set-valued input and yields predictions for the entire test set in a single forward pass. TabPFN is a Prior-Data Fitted Network (PFN) and is trained offline once, to approximate Bayesian inference on synthetic datasets drawn from our prior. This prior incorporates ideas from causal reasoning: It entails a large space of structural causal models with a preference for simple structures. On the 18 datasets in the OpenML-CC18 suite that contain up to 1 000 training data points, up to 100 purely numerical features without missing values, and up to 10 classes, we show that our method clearly outperforms boosted trees and performs on par with complex state-of-the-art AutoML systems with up to 230× speedup. This increases to a 5 700× speedup when using a GPU. We also validate these results on an additional 67 small numerical datasets from OpenML. We provide all our code, the trained TabPFN, an interactive browser demo and a Colab notebook at https://github.com/automl/TabPFN.

PaperSource

Papers archive 2025-07-28

26 shown of 26, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 44 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
In-Context Learning16
tabular-classification6
AutoML3
Bayesian Inference3
Meta-Learning3
Transfer Learning3
Classification2
Language Modeling2
Language Modelling2
Prompt Engineering2
Retrieval2
feature selection2
All1
Computational Efficiency1
Data Augmentation1
Data Valuation1
Deep Learning1
Denoising1
Density Estimation1
Disease Prediction1

Usage over time archive 2025-07-28

Papers per year tagged with TABPFN: 2022 to 2025, peak 13 13 0 2022: 1 paper 2022 2023: 3 papers 2023 2024: 13 papers 2024 2025: 9 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (26 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Deep Tabular Learning

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