arXiv Machine Learning June 18, 2026 By Narges Saeednejad, Jamie Ellen Padgett

Bridging Data Gaps in Structural Fragility Modeling through Transfer Learning: Methodology and Case Studies

Read the original on arXiv Machine Learning →

arXiv:2606. 18567v1 Announce Type: cross Abstract: This paper presents a methodology-centered transfer learning framework for fragility adaptation under domain shift, class imbalance, and scarce target labels while preserving engineering interpretability and supporting decision-making under uncertainty.

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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Hierarchical Projection for Adaptive Knowledge Transfer

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Distributionally Robust Transfer Learning with Structurally Missing Covariates, with Application to Cross-National Cardiac Arrest Prediction

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arXiv Machine Learning

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