5 papers · 1 filter
Beyond IID: How General Are Tabular Foundation Models, Really?
Lennart Purucker, Andrej Tschalzev, Nick Erickson +7
Foundation models for predictive machine learning on tabular data have recently gained significant traction in academia and industry. Research communities across disciplines are in…
Towards Evaluating Data Priors for Tabular Foundation Models
Zeynep Türkmen, KürÅat Kaya, Alexander Pfefferle +1
Data-generating priors are a central component of tabular foundation models because they define the task distribution used during pretraining. However, priors are rarely evaluated…
Towards Pretraining Text Encoders for TabPFN
Mustafa Tajjar, Alexander Pfefferle, Lennart Purucker +1
Tabular foundation models, such as TabPFN, achieve strong performance on tabular datasets with numerical and categorical data, but do not natively handle high-cardinality text feat…
Speedrunning Tabular Foundation Model Pretraining
Salih Bora Ozturk, Alexander Pfefferle, Frank Hutter
Pretraining cost is a major bottleneck for research on tabular foundation models, slowing the iteration cycle for new architectures, priors, and optimization ideas. Yet the communi…
nanoTabPFN: A Lightweight and Educational Reimplementation of TabPFN
Alexander Pfefferle, Johannes Hog, Lennart Purucker +1
Tabular foundation models such as TabPFN have revolutionized predictive machine learning for tabular data. At the same time, the driving factors of this revolution are hard to unde…