104 citations · 145 across the 11 of their papers we have counts for
14 papers
Attention Quantization for Tabular Foundation Models
Jonas M. Kübler, Benjamin Jäger, Klemens Flöge +2
With the recent rise and adoption of tabular foundation models, optimizing their inference performance becomes an emerging field for efficiency research. While the models are archi…
Advancing Open and Reproducible Relational Learning: RelArena-, TabPFN-Rel and RPI
Adrian Hayler, Klemens Flöge, Alan Arazi +44
This first release of Prior Labs in relational learning shows our continued commitment to open science. We open-source three pieces of software that we expect to accelerate researc…
TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models
Léo Grinsztajn, Klemens Flöge, Oscar Key +23
The first tabular foundation model, TabPFN, and its successor TabPFNv2 have impacted tabular AI substantially, with dozens of methods building on it and hundreds of applications ac…
Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data
Anurag Garg, Muhammad Ali, Noah Hollmann +3
Foundation models for tabular data, like TabPFN, achieve strong performance on small datasets when pre-trained solely on synthetic data. We show that this performance can be signif…
FairPFN: A Tabular Foundation Model for Causal Fairness
Jake Robertson, Noah Hollmann, Samuel Müller +2
Machine learning (ML) systems are utilized in critical sectors, such as healthcare, law enforcement, and finance. However, these systems are often trained on historical data that c…
Do-PFN: In-Context Learning for Causal Effect Estimation
Jake Robertson, Arik Reuter, Siyuan Guo +3
Estimation of causal effects is critical to a range of scientific disciplines. Existing methods for this task either require interventional data, knowledge about the ground truth c…
Position: The Future of Bayesian Prediction Is Prior-Fitted
Samuel Müller, Arik Reuter, Noah Hollmann +2
Training neural networks on randomly generated artificial datasets yields Bayesian models that capture the prior defined by the dataset-generating distribution. Prior-data Fitted N…
Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data
Kai Helli, David Schnurr, Noah Hollmann +2
While most ML models expect independent and identically distributed data, this assumption is often violated in real-world scenarios due to distribution shifts, resulting in the deg…
Bayes' Power for Explaining In-Context Learning Generalizations
Samuel Müller, Noah Hollmann, Frank Hutter
Traditionally, neural network training has been primarily viewed as an approximation of maximum likelihood estimation (MLE). This interpretation originated in a time when training…
FairPFN: Transformers Can do Counterfactual Fairness
Jake Robertson, Noah Hollmann, Noor Awad +1
Machine Learning systems are increasingly prevalent across healthcare, law enforcement, and finance but often operate on historical data, which may carry biases against certain dem…
PFNs4BO: In-Context Learning for Bayesian Optimization
Samuel Müller, Matthias Feurer, Noah Hollmann +1
In this paper, we use Prior-data Fitted Networks (PFNs) as a flexible surrogate for Bayesian Optimization (BO). PFNs are neural processes that are trained to approximate the poster…
Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering
Noah Hollmann, Samuel Müller, Frank Hutter
As the field of automated machine learning (AutoML) advances, it becomes increasingly important to incorporate domain knowledge into these systems. We present an approach for doing…
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
Noah Hollmann, Samuel Müller, Katharina Eggensperger +1
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 w…
Transformers Can Do Bayesian Inference
Samuel Müller, Noah Hollmann, Sebastian Pineda Arango +2
Currently, it is hard to reap the benefits of deep learning for Bayesian methods, which allow the explicit specification of prior knowledge and accurately capture model uncertainty…