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TabPFN-3.5 is released, claiming state-of-the-art performance for tabular data beyond IID small data settings, with features like handling grouped and temporal data, uncertainty calibration, and faster inference.
Prior Labs releases open-source tools including RelArena-α, TabPFN-Rel, and RPI to advance reproducible relational learning, providing a unified benchmark framework and a TabPFN-based model.
This paper investigates context sampling for TabPFN on small tabular datasets, finding that context diversity and coverage are more important than distribution matching for accuracy, and that random sampling is effective.
This paper introduces GOTabPFN, a method that combines Graph-guided Ordering with Local Refinement (GO-LR) and Neuro-Inspired Subunit Compression (NSC) to make small tabular foundation models effective for high-dimensional, low-sample-size prediction without retraining large backbones.
This paper evaluates the use of tabular foundation models, particularly TabPFN, for calibrating near-infrared spectroscopy data. The model shows strong performance on regression and classification tasks compared to traditional chemometric methods.
TabPFN is introduced as a foundation model specifically designed for tabular data by PriorLabs.