arXiv Machine Learning By Jiahao Shi, Omar Hagrass, Jason M. Klusowski

Coupled Training with Privileged Information and Unlabeled Data

Read the original on arXiv Machine Learning →

arXiv:2605. 23268v2 Announce Type: replace-cross Abstract: In many prediction problems, we have extra information during training (for example, measurements that are expensive or slow to collect) that will not be available when the model is deployed.

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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.

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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.

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Generalization Can Emerge in Tabular Foundation Models From a Single Table

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