Author

Alex Labach

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#artificial intelligence Nov 2025

Generalization Can Emerge in Tabular Foundation Models From a Single Table

This work systematically pre-training and evaluating on many diverse datasets and analyzes what aspects of the data are most important for building a Tabular Foundation Model (TFM) generalizing across domains to show that the number and quality of tasks one can construct from a dataset is key to downstream performance.

Junwei Ma, Nour Shaheen, Alex Labach et al. · arXiv.org · 4 citations

Preprint Aug 2026

TabDPT-Turbo: Efficient In-Context Learning for Tabular Prediction

This work adopts an alternate approach, sticking with row-based attention while incorporating long context pre-training to eliminate the need for retrieval in TabDPT-Turbo, a model that provides comparable default performance to TabDPT v1.1 on TabArena-Lite, CC18, and CTR23, at orders of magnitude faster.

Rasa Hosseinzadeh, Alex Labach, Zexin Xue et al. · 4 citations

#machine learning Preprint Aug 2026

Understanding the Surprising Generalization Properties of Tabular Foundation Models

A task-centric, retrieval-based perspective is offered for how TFMs generalize: it is believed that tabular in-context generalization is largely retrieval-based, and good models are those that learn to identify relevant examples in the provided context and aggregate them well.

Nour Shaheen, Junwei Ma, Alex Labach et al. · 3 citations