Preprint2025
On finetuning tabular foundation models
Unknown
This paper investigates the fine-tuning of tabular foundation models, particularly TabPFNv2, and compares their performance against traditional GBDT methods.
0Jun 1, 2025
arXiv
AI Research
AI & ML Research Papers
A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
All Papers
3 papersPreprintarXiv.org2025
Tabpfn-2.5: Advancing the state of the art in tabular foundation models
L'eo Grinsztajn, Klemens Floge, Oscar Key, et al.
Tabpfn-2.5 advances tabular foundation models with new techniques including retrieval, fine-tuning, and novel architectures, aiming to define the future of tabular data systems.
118Nov 1, 2025Fine TuningRetrieval Augmented Generation
arXiv
JournalNature2025
Accurate predictions on small data with a tabular foundation model
Noah Hollmann, Samuel Müller, Lennart Purucker, et al.
TabPFN, a transformer-based tabular foundation model, outperforms all prior methods on small datasets (up to 10k samples) in 2.8 seconds, surpassing tuned ensembles trained for hours.
1.2kJan 8, 2025TransformersFine Tuning