arXiv Machine Learning
Jul 17
GenTL: A General Transfer Learning Model for Building Thermal Dynamics
arXiv:2501. 13703v2 Announce Type: replace-cross Abstract: Transfer Learning (TL) is an emerging field in modeling building thermal dynamics.
Bridging Data Gaps in Structural Fragility Modeling through Transfer Learning: Methodology and Case Studies
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.
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arXiv Machine Learning
Jun 9
Hierarchical Projection for Adaptive Knowledge Transfer
arXiv:2606. 08691v1 Announce Type: new Abstract: Modern data-driven applications increasingly involve learning from multiple heterogeneous sources, where a target dataset is limited but related information is available across domains.
arXiv Machine Learning
Jul 23
Generative AI-enhanced Probabilistic Multi-Fidelity Surrogate Modeling Via Transfer Learning
arXiv:2602. 00072v2 Announce Type: replace Abstract: The performance of machine learning surrogates is critically dependent on data quality and quantity.
arXiv Machine Learning
Jun 9
Transfer learning for causal forest
arXiv:2606. 07693v1 Announce Type: cross Abstract: Transfer learning addresses the challenge of transfering knowledge from one domain to another.
arXiv AI
Aug 6
Distributionally Robust Transfer Learning with Structurally Missing Covariates, with Application to Cross-National Cardiac Arrest Prediction
arXiv:2605. 24212v2 Announce Type: replace-cross Abstract: Deploying clinical prediction models across healthcare systems often fails when key training covariates are unavailable at deployment and labeled outcomes are limited in the target domain.
arXiv Machine Learning
6d ago
Heterogeneous transfer learning for high-dimensional regression with feature mismatch
arXiv:2412. 18081v3 Announce Type: replace-cross Abstract: We study Heterogeneous Transfer Learning (HTL) for high-dimensional regression with differing feature sets.
arXiv Machine Learning
Jul 7
Transfer Learning in High-dimensional Ising Models
arXiv:2607. 03005v1 Announce Type: new Abstract: In high-dimensional Ising model estimation, target sample sizes are often limited, and effectively using auxiliary binary datasets of unknown relevance remains challenging.
arXiv Machine Learning
Jun 5
Harnessing Source Heterogeneity for Cluster-Structured Transfer Learning
arXiv:2606. 05258v1 Announce Type: cross Abstract: Transfer learning is a natural strategy when a target population has limited data but multiple related auxiliary sources are available.
arXiv AI
Jul 7
A Bayesian Framework for Evaluating Scenario Compatibility in Generative Population Synthesis
arXiv:2607. 03190v1 Announce Type: cross Abstract: Scenario-based transportation analysis specifies future assumptions through aggregate population targets, whereas generative population synthesis models produce detailed individual-level realizations.
arXiv AI
Jul 29
Localized Adaptation Reveals Distinct Learning Signatures in Transformers
arXiv:2607. 25663v1 Announce Type: new Abstract: Transformer adaptation is typically distributed across model depth, even when the intended change is narrow.
arXiv Machine Learning
Aug 11
Deep Learning Imputation of Missing Radius of Maximum Winds (Rmax) Values in Tropical Cyclone Best-Track Data
arXiv:2608. 09683v1 Announce Type: new Abstract: Probabilistic coastal hazard assessments require accurate characterization of tropical cyclone (TC) parameters, yet datasets often contain missing records for the radius of maximum winds (Rmax), a key variable in Joint Probability Method analyses.
arXiv Machine Learning
Jul 7
Context-Constrained Transfer Learning for Tabular Foundation Models via Data Distillation
arXiv:2607. 04809v1 Announce Type: cross Abstract: Tabular Foundation Models (TFMs) have demonstrated strong empirical performance as black-box inference engines through in-context learning.