# Hierarchical Projection for Adaptive Knowledge Transfer

arXiv: https://arxiv.org/abs/2606.08691 · HTML: https://mlsanity.com/paper/2606.08691/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 27 references that are in the map.
- First posted on arXiv 7 Jun 2026.

## Builds on (most cited first, 15 of 15)

- [Transfer Learning for High-dimensional Linear Regression: Prediction, Estimation, and Minimax Optimality](https://mlsanity.com/paper/2006.10593/) (2020)
- [Transfer Learning under High-dimensional Generalized Linear Models](https://mlsanity.com/paper/2105.14328/) (2021)
- [Targeting Underrepresented Populations in Precision Medicine: A Federated Transfer Learning Approach](https://mlsanity.com/paper/2108.12112/) (2021)
- [Robust angle-based transfer learning in high dimensions](https://mlsanity.com/paper/2210.12759/) (2022)
- [Heterogeneity-aware and communication-efficient distributed statistical inference](https://mlsanity.com/paper/1912.09623/) (2019)
- [Heterogeneous transfer learning for high-dimensional regression with feature mismatch](https://mlsanity.com/paper/2412.18081/) (2024)
- [Representation Retrieval Learning for Heterogeneous Data Integration](https://mlsanity.com/paper/2503.09494/) (2025)
- [Covariate-Elaborated Robust Partial Information Transfer with Conditional Spike-and-Slab Prior](https://mlsanity.com/paper/2404.03764/) (2024)
- [Bayesian High-dimensional Grouped-regression using Sparse Projection-posterior](https://mlsanity.com/paper/2411.15713/) (2024)
- [Bayesian Transfer Learning for Enhanced Estimation and Inference](https://mlsanity.com/paper/2412.02986/) (2024)
- [Improving RCT-Based CATE Estimation Under Covariate Mismatch via Double Calibration](https://mlsanity.com/paper/2603.17066/) (2026)
- [Unit Information Prior for Adaptive Information Borrowing from Multiple Historical Datasets](https://mlsanity.com/paper/2102.00796/) (2021)
- [Heterogeneous Transfer Learning for Building High-Dimensional Generalized Linear Models with Disparate Datasets](https://mlsanity.com/paper/2312.12786/) (2023)
- [Bayesian High-dimensional Linear Regression with Sparse Projection-posterior](https://mlsanity.com/paper/2410.16577/) (2024)
- [Improving RCT-Based Treatment Effect Estimation Under Covariate Mismatch via Calibrated Alignment](https://mlsanity.com/paper/2603.19186/) (2026)

## Abstract (arXiv)

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 sources using data-driven weights, capturing global alignment between each source and the target; second, we refine this borrowing through a posterior-projection step that operates at the feature level, selectively retaining coordinates that exhibit local agreement with the target signal. This two-stage design enables the method to simultaneously perform source selection and feature selection, thereby mitigating negative transfer while preserving interpretability. ProjectionTL provides a principled approach to integrating heterogeneous data across domains, bridging statistical modeling and modern machine learning paradigms for robust and interpretable transfer. Through simulations and real-world biomedical applications, we demonstrate improved accuracy, stability, and interpretability compared to existing methods. Our framework offers a scalable and generalizable strategy for trustworthy cross-domain learning in high-dimensional settings.