ArticleJournal of the Royal Statistical Society. Series B, Statistical methodology2022
Transfer Learning for High-Dimensional Linear Regression: Prediction, Estimation and Minimax Optimality.
Article in Journal of the Royal Statistical Society. Series B, Statistical methodology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.
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Who cites it
58 citing papers in PubMed.
- TL-HDMR: a transfer learning framework for advancing equitable causal inference reveals metabolic signatures of stroke across multiple ancestries.Briefings in bioinformatics · 2026Article
- Hybrid Quantile-Expectile Error Layers for Technical-Efficiency Recovery in Stochastic Frontier Analysis.Entropy (Basel, Switzerland) · 2026Article
- Machine learning surrogate forward models for biomechanical laryngeal control.The Journal of the Acoustical Society of America · 2026Article
- Multiple Summary Knowledge-Based Disease Prevalence Estimation for Group Testing Data.Statistics in medicine · 2026Article
- Double shrinkage transfer causal learning: An application to alzheimer's disease.PLoS computational biology · 2026Article
- Reluctant Transfer Learning in Penalized Regressions for Individualized Treatment Rules Under Effect Heterogeneity.Statistics in medicine · 2026Article
- Machine learning surrogate forward models for biomechanical laryngeal control.bioRxiv : the preprint server for biology · 2026Article
- Praxis-BGM: clustering of omics data using semi-supervised transfer learning for Gaussian mixture models via natural-gradient variational inference.Bioinformatics (Oxford, England) · 2026Article
- Transfer Learning for Moderate-Dimensional Ridge-Regularized Robust Linear Regression.Entropy (Basel, Switzerland) · 2026Article
- Efficient collaborative learning of the average treatment effect.Biometrics · 2026Article
- MRBEE-TL: improving causal effect estimation in multi-ancestry multivariable Mendelian randomization with transfer learning.Genome biology · 2026Article
- BRIDGING THE GAP: ENHANCING THE GENERALIZABILITY OF EPIGENETIC CLOCKS THROUGH TRANSFER LEARNING.The annals of applied statistics · 2026Article
- LtransHeteroGGM: local transfer learning for Gaussian graphical model-based heterogeneity analysis.Bioinformatics (Oxford, England) · 2026Article
- Improving polygenic score prediction for underrepresented groups through transfer learning.Nature communications · 2026Article
- Cross-ancestry information transfer framework improves protein abundance prediction and protein-trait association identification.Briefings in bioinformatics · 2026Article
- Transfer Learning via Regularized Random-effects Linear Discriminant Analysis.Journal of machine learning research : JMLR · 2026Article
- Classification Under Local Differential Privacy with Model Reversal and Model Averaging.Journal of machine learning research : JMLR · 2026Article
- High-dimensional Parameter Transfer With Fused-Regularizer.Journal of machine learning research : JMLR · 2026Article
- Robust transfer learning for individualized treatment rules in the presence of missing data.Biostatistics (Oxford, England) · 2025Article
- On the Connections Among Three Transfer Learning Paradigms.Stat (International Statistical Institute) · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
This paper considers estimation and prediction of a high-dimensional linear regression in the setting of transfer learning where, in addition to observations from the target model, auxiliary samples from different but possibly related regression models are available. When the set of informative auxiliary studies is known, an estimator and a predictor are proposed and their optimality is established. The optimal rates of convergence for prediction and estimation are faster than the corresponding rates without using the auxiliary samples. This implies that knowledge from the informative auxiliary samples can be transferred to improve the learning performance of the target problem. When the set of informative auxiliary samples is unknown, we propose a data-driven procedure for transfer learning, called Trans-Lasso, and show its robustness to non-informative auxiliary samples and its efficiency in knowledge transfer. The proposed procedures are demonstrated in numerical studies and are applied to a dataset concerning the associations among gene expressions. It is shown that Trans-Lasso leads to improved performance in gene expression prediction in a target tissue by incorporating data from multiple different tissues as auxiliary samples.
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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 OpenQuestion graph.