ArticleJournal of the Royal Statistical Society. Series B, Statistical methodology2025
Robust angle-based transfer learning in high dimensions.
Article in Journal of the Royal Statistical Society. Series B, Statistical methodology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- ReFIT: Federated Transfer Learning for Sequential Prediction and Uncertainty Quantification Using Streaming EHR Data.Statistics in biosciences · 2026Article
- BRIDGING THE GAP: ENHANCING THE GENERALIZABILITY OF EPIGENETIC CLOCKS THROUGH TRANSFER LEARNING.The annals of applied statistics · 2026Article
- On the Connections Among Three Transfer Learning Paradigms.Stat (International Statistical Institute) · 2025Article
- TransferTWAS: A transfer learning framework for cross-tissue transcriptome-wide association study.American journal of human genetics · 2025Article
- Transdiagnostic Polygenic Risk Models for Psychopathology and Comorbidity: Cross-Ancestry Analysis in themedRxiv : the preprint server for health sciences · 2025Article
- Enhancing Genetic Risk Prediction through Federated Semi-Supervised Transfer Learning with Inaccurate Electronic Health Record Data.Statistics in biosciences · 2024Article
- EFFICIENT AND MULTIPLY ROBUST RISK ESTIMATION UNDER GENERAL FORMS OF DATASET SHIFT.Annals of statistics · 2024Article
- Multi-Task Learning with Summary Statistics.Advances in neural information processing systems · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Transfer learning improves target model performance by leveraging data from related source populations, especially when target data are scarce. This study addresses the challenge of training high-dimensional regression models with limited target data in the presence of heterogeneous source populations. We focus on a practical setting where only parameter estimates of pretrained source models are available, rather than individual-level source data. For a single source model, we propose a novel angle-based transfer learning (angleTL) method that leverages concordance between source and target model parameters. AngleTL adapts to the signal strength of the target model, unifies several benchmark methods, and mitigates negative transfer when between-population heterogeneity is large. We extend angleTL to incorporate multiple source models, accounting for varying levels of relevance among them. Our high-dimensional asymptotic analysis provides insights into when a source model benefits the target model and demonstrates the superiority of angleTL over other methods. Extensive simulations validate these findings and highlight the feasibility of applying angleTL to transfer genetic risk prediction models across multiple biobanks.
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.