17 papers · 1 filter
TimEE: End-to-end Time Series Classification via In-Context Learning
Jaris Küken, Shi Bin Hoo, Martin Mráz +2
Time series classification (TSC) is dominated by a two-stage paradigm: train a feature encoder -- either from scratch on the target dataset or via pretraining on large corpora -- a…
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…
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…
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…
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…