arXiv Machine Learning August 14, 2026 By Mahboobe Jadid, Melika Rezaye Garkani, Ali Mousavi

Balanced Adaptive Prototype Selection for Scalable TabPFN Inference on Large-Scale Tabular Data

Read the original on arXiv Machine Learning →

arXiv:2608. 12989v1 Announce Type: new Abstract: Pretrained tabular foundation models have demonstrated strong predictive capability; however, their application to large-scale datasets remains constrained by the limited inference context.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

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

arXiv:2608. 01400v1 Announce Type: new Abstract: Tabular foundation models, driven by in-context learning, have rapidly grown in quality and popularity.

By Rasa Hosseinzadeh, Alex Labach, Zexin Xue, Shuyi Han, Valentin Thomas, Anthony L. Caterini

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TabPack: Efficient Hyperparameter Ensembles for Tabular Deep Learning

arXiv:2607. 05380v1 Announce Type: new Abstract: In deep learning for tabular data, efficient ensembles of multilayer perceptrons (MLPs) have recently emerged as effective and practical architectures.

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TACTICL: Task-Aware Compression of Tabular ICL Models

arXiv:2608. 10837v1 Announce Type: cross Abstract: The strong performance of foundation models for tabular tasks comes at substantial inference costs.

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llms benchmarks

arXiv Machine Learning

Jun 8

TabSwift: An Efficient Tabular Foundation Model with Row-Wise Attention

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By Si-Yang Liu, Han-Jia Ye

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arXiv Machine Learning

Jun 2

When Tabular Foundation Models Transfer Across Modalities: A Systematic Evaluation Across 95 Datasets, 7 Modalities, and Two Regimes

arXiv:2606. 02106v1 Announce Type: new Abstract: We present a single classification pipeline that combines an Equiangular Tight Frame (ETF) preprocessing stage with a tabular foundation model for in-context inference, applied identically across modalities once data is mapped to fixed vector representations.

By Julien Lafrance

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arXiv Machine Learning

Jul 31

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arXiv:2607. 27546v1 Announce Type: new Abstract: Tabular Foundation Models, such as TabPFN, have received a large amount of recent attention due to their performance on in-context tabular machine learning tasks, which often exceeds classical baselines.

By Shuting Luo, Monika Mikhail Kanaan, Cameron Gordon, Anna Leontjeva, Simon Lucey

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Related stories

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

TabPack: Efficient Hyperparameter Ensembles for Tabular Deep Learning

TACTICL: Task-Aware Compression of Tabular ICL Models

TabSwift: An Efficient Tabular Foundation Model with Row-Wise Attention

When Tabular Foundation Models Transfer Across Modalities: A Systematic Evaluation Across 95 Datasets, 7 Modalities, and Two Regimes

Memory Efficient Tabular Foundation Models