arXiv AI July 7, 2026 By AlJawharh S. AlOtaibi, Mohamed Eltahir, Jude AlSubaie

When Does Small Data Work? Accuracy and Efficiency Trade-offs Between Tabular Foundation Models and Conventional Methods for Crowd-State Classification at Hajj and Umrah

arXiv:2607. 04013v1 Announce Type: cross Abstract: Learning from few labeled examples is a central challenge in tabular machine learning, and it becomes the binding constraint in domains where labeling is costly, such as crowd monitoring during Hajj and Umrah.

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

arXiv Machine Learning

Jul 30

The Advantage of Fine-Grained Training

arXiv:2509. 05130v2 Announce Type: replace Abstract: In classification problems, models are trained to predict a class label based on the input data features.

By Davide Pirovano, Federico Milanesio, Michele Caselle, Piero Fariselli, Matteo Osella

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arXiv AI

Jun 4

Learning to Evaluate: Cost-Effective Model Evaluation on Unlabeled Data with Meta-Learning

arXiv:2605. 23595v2 Announce Type: replace-cross Abstract: The rapid advancement of machine learning has led to an unprecedented expansion of model ecosystems, making it increasingly difficult to assess the reliability of newly released models on unseen and unlabeled data.

By Trinh Pham, Viet Huynh, Hongzhi Yin, Quoc Viet Hung Nguyen, Thanh Tam Nguyen

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arXiv Machine Learning

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TabH2O: A Unified Foundation Model for Tabular Prediction

arXiv:2605. 18383v2 Announce Type: replace Abstract: We present TabH2O, a foundation model for tabular data that performs classification and regression in a single forward pass via in-context learning.

By Pascal Pfeiffer, Dmitry Gordeev, Mathias M\"uller, Laura Fink, Joan Salv\`a Soler, Mark Landry, Branden Murray, Marcos V. Conde, Sri Satish Ambati

llms benchmarks

arXiv AI

Jul 22

Wisdom of LLM Crowds: Aggregation and Contamination in Language Model Ensembles

arXiv:2607. 18269v1 Announce Type: new Abstract: The wisdom of crowds -- the finding that aggregating judgments across individuals often outperforms the best individual -- has been extensively studied with human forecasters.

By Igor Douven

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arXiv Machine Learning

Jul 2

Explaining Tabular Foundation Model Differences Through Meta-Features

arXiv:2605. 28418v3 Announce Type: replace Abstract: With the rise of tabular foundation models alongside traditional models still performing well on many tasks, choosing the right model for a tabular dataset remains difficult.

By Markus Herre, Andrej Tschalzev, Sascha Marton, Christian Bartelt

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arXiv Machine Learning

Jul 16

Foundation Models for Credit Risk Prediction: A Game Changer?

arXiv:2605. 18147v2 Announce Type: replace Abstract: Predictive models play a pivotal role in credit risk management, guiding critical decisions through accurate estimation of default probabilities and losses.

By Bart Baesens, Andreas Goethals, Stefan Lessmann, Simon De Vos, Cristi\'an Bravo, David Martens, Victor Medina-Olivares, Christophe Mues, Maria Oskarsd\'ottir, Seppe vanden Broucke, Tony Van Gestel, Tim Verdonck, Wouter Verbeke

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