ArticleStat (International Statistical Institute)2025
On the Connections Among Three Transfer Learning Paradigms.
Article in Stat (International Statistical Institute), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- ReFIT: Federated Transfer Learning for Sequential Prediction and Uncertainty Quantification Using Streaming EHR Data.Statistics in biosciences · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
We examine the solution paths of three transfer learning estimators within the context of linear models. Our analysis identifies a general solution path that characterizes how transfer learning estimators interpolate between the target and source estimators. This involves a change of basis and entry-wise weighting functions, with existing transfer learning methods identified as special cases. The proposed framework reveals connections and equivalences among transfer learning approaches, offering valuable insights for designing future estimators with improved control over the learning process. It extends beyond traditional penalized regression and gradient descent techniques and holds the potential for generalization to nonlinear cases. Extensive simulations validate the theoretically derived solution paths, and their practical utility is demonstrated by the improvement in risk prediction for end-stage renal disease in Hispanic populations.
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What OpenQuestion 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 OpenQuestion graph.