Harnessing Source Heterogeneity for Cluster-Structured Transfer Learning
Transfer learning is a natural strategy when a target population has limited data but multiple related auxiliary sources are available. A central difficulty is source heterogeneity: auxiliary sources may not be equally useful, and their usefulness may vary in a structured, cluster-like fashion. Existing transfer-learning methods often reduce source selection to a binary informative/non-informative decision, overlooking subgroups of sources with differential transferability. Motivated by a suicide-risk study using data from the Connecticut Hospital Information Management Exchange (CHIME), comprising 636,758 patients across 27 hospitals, we propose Trans-GLMC, a cluster-structured transfer-...
| Authors | Xiaohui Yin, Jun Jin, Shane J. Sacco et al. |
|---|---|
| Primary category | stat.ML |
| Published | 3 June 2026 |
| Last updated at source | 3 June 2026 |
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