Prior-data Fitted Networks (PFNs, https://arxiv.org/abs/2112.10510) are transformer-based models trained to approximate Bayesian prediction. They are trained to do this via supervised in-context learning on datasets randomly drawn from a prior. Our priors can in general be described by a function that samples datasets, or more generally a batch of datasets. The PFN is then trained to predict a hold-out set of labels, given the rest of the dataset.

The pseudo code for a simple prior that would yield a PFN that does 1d ridge regression on datasets with 100 elements, could be something like this:

def get_dataset_sample():
    x = RandomUniform(100,1)
    a = RandomNormal()
    b = RandomNormal()
    y = a * x + b
    return x, y

Check out our tutorial to train your own ridge regression PFN.

This way of installing allows you to use the package everywhere and still be able to edit files. You should use a pytorch compatible python version (oftentimes they don't support the latest version).

git clone https://github.com/automl/PFNs.git
cd PFNs
pip install -e .

We use a CI, the parts of which you can run before locally:

1. Tests: To run tests simply use pytest tests.
2. Formatting: Use pre-commit (install with pip install pre-commit, then pre-commit install) and run manually it with pre-commit run --all-files --show-diff-on-failure

Check out our Getting Started Colab.

• Code to train models with a variety of priors
• The feature-wise architecture from TabPFNv2 as well as the traditional PFN architecture
• A lot of normalizers to encode features well
• Some code to run Bayesian Optimization experiments

There is a BO version of this repo, with pretrained models at github.com/automl/PFNs4BO. The two repos share a lot of the code, but the other is not anymore actively maintained. You can also train your own models with our tutorial notebook here.

To run all BayesOpt experiments, please install this package with the benchmarks option:

pip install -e .[benchmarks]

This repository is frozen at the state of the submission all funcionality is copied to the actively maintained repository github.com/automl/PFNs

This repository contains the code for the paper "Bayes' Power for Explaining In-Context Learning Generalizations".

Install in editable mode:

pip install -e .

We have a set of notebooks in this repository to reproduce the results of our paper.

• To reproduce the main ICL experiments, use the notebook discrete_bayes.ipynb.
• To run the Tiny-MLP generalization experiments, where we evaluate extrapolation, use the notebook Tiny_MLP_Generalization.ipynb.
• To run the Coin-Flipping experiments, where we show that the true posterior converges to the wrong probability, use the notebook Cointhrowing_converging_to_wrong_posterior.ipynb.
• To see the GP converging to the wrong solution for a step function, use the notebook GP_fitting_a_step.ipynb.

PFNs were introduced in

@inproceedings{
    muller2022transformers,
    title={Transformers Can Do Bayesian Inference},
    author={Samuel M{\"u}ller and Noah Hollmann and Sebastian Pineda Arango and Josif Grabocka and Frank Hutter},
    booktitle={International Conference on Learning Representations},
    year={2022},
    url={https://openreview.net/forum?id=KSugKcbNf9}
}

Training PFNs on tabular data (TabPFN) was enhanced in

@inproceedings{
  hollmann2023tabpfn,
  title={Tab{PFN}: A Transformer That Solves Small Tabular Classification Problems in a Second},
  author={Noah Hollmann and Samuel M{\"u}ller and Katharina Eggensperger and Frank Hutter},
  booktitle={The Eleventh International Conference on Learning Representations},
  year={2023},
  url={https://openreview.net/forum?id=cp5PvcI6w8_}
}

The BO version of PFNs was introduced in

@article{muller2023pfns,
  title={PFNs4BO: In-Context Learning for Bayesian Optimization},
  author={M{\"u}ller, Samuel and Feurer, Matthias and Hollmann, Noah and Hutter, Frank},
  journal={arXiv preprint arXiv:2305.17535},
  year={2023}
}

The "Bayes' Power for Explaining In-Context Learning Generalizations" is

@article{muller2024bayes,
  title={Bayes' Power for Explaining In-Context Learning Generalizations},
  author={M{\"u}ller, Samuel and Hollmann, Noah and Hutter, Frank},
  journal={arXiv preprint arXiv:2410.01565},
  year={2024}
}

The new architecture, which we support via config.model.features_per_group = <some small positive int, like 1> + config.model.attention_between_features = True.

@article{hollmann2025accurate,
  title={Accurate predictions on small data with a tabular foundation model},
  author={Hollmann, Noah and M{\"u}ller, Samuel and Purucker, Lennart and Krishnakumar, Arjun and K{\"o}rfer, Max and Hoo, Shi Bin and Schirrmeister, Robin Tibor and Hutter, Frank},
  journal={Nature},
  volume={637},
  number={8045},
  pages={319--326},
  year={2025},
  publisher={Nature Publishing Group UK London}
}