1 citations · 1 across the 5 of their papers we have counts for
12 papers
Causal Data Augmentation for Robust Fine-Tuning of Tabular Foundation Models
Magnus Bühler, Lennart Purucker, Frank Hutter
Fine-tuning tabular foundation models (TFMs) under data scarcity is challenging, as early stopping on even scarcer validation data often fails to capture true generalization perfor…
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…
Does TabPFN Understand Causal Structures?
Omar Swelam, Lennart Purucker, Jake Robertson +3
Causal discovery is fundamental for multiple scientific domains, yet extracting causal information from real world data remains a significant challenge. Given the recent success on…
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…
Agentic NL2SQL to Reduce Computational Costs
Dominik Jehle, Lennart Purucker, Frank Hutter
Translating natural language queries into SQL queries (NL2SQL or Text-to-SQL) has recently been empowered by large language models (LLMs). Using LLMs to perform NL2SQL methods on a…
TempoPFN: Synthetic Pre-training of Linear RNNs for Zero-shot Time Series Forecasting
Vladyslav Moroshan, Julien Siems, Arber Zela +2
Foundation models for zero-shot time series forecasting face challenges in efficient long-horizon prediction and reproducibility, with existing synthetic-only approaches underperfo…