Hierarchical Projection for Adaptive Knowledge Transfer
Modern data-driven applications increasingly involve learning from multiple heterogeneous sources, where a target dataset is limited but related information is available across domains. Naively combining these sources can degrade performance when relevance varies or spurious signals are present, posing a fundamental challenge for trustworthy cross-domain learning. We propose Projection Transfer Learning (ProjectionTL), a unified framework that integrates hierarchical Bayesian modeling with adaptive projection for selective knowledge transfer. The key idea is to decouple transfer at two levels: first, we construct a source-guided hierarchical prior that aggregates information across source...
| Authors | Samhita Pal, Tian Gu |
|---|---|
| Primary category | cs.LG |
| Published | 7 June 2026 |
| Last updated at source | 28 June 2026 |
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