# Covariate-Elaborated Robust Partial Information Transfer with Conditional Spike-and-Slab Prior

arXiv: https://arxiv.org/abs/2404.03764 · HTML: https://mlsanity.com/paper/2404.03764/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 43 references that are in the map.
- First posted on arXiv 30 Mar 2024.

## Builds on (most cited first, 16 of 16)

- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [A Survey on Negative Transfer](https://mlsanity.com/paper/2009.00909/) (2020)
- [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)
- [Variational Bayes for high-dimensional linear regression with sparse priors](https://mlsanity.com/paper/1904.07150/) (2019)
- [Bayesian linear regression with sparse priors](https://mlsanity.com/paper/1403.0735/) (2014)
- [Transfer Learning in Large-scale Gaussian Graphical Models with False Discovery Rate Control](https://mlsanity.com/paper/2010.11037/) (2020)
- [On the Computational Complexity of High-Dimensional Bayesian Variable Selection](https://mlsanity.com/paper/1505.07925/) (2015)
- [Optimal Bayesian Transfer Learning](https://mlsanity.com/paper/1801.00857/) (2018)
- [Spike and slab variational Bayes for high dimensional logistic regression](https://mlsanity.com/paper/2010.11665/) (2020)
- [Bayesian Bootstrap Spike-and-Slab LASSO](https://mlsanity.com/paper/2011.14279/) (2020)
- [Transfer Learning for High-dimensional Quantile Regression via Convolution Smoothing](https://mlsanity.com/paper/2212.00428/) (2022)
- [Transferring model structure in Bayesian transfer learning for Gaussian process regression](https://mlsanity.com/paper/2101.06884/) (2021)
- [Scalable Spike-and-Slab](https://mlsanity.com/paper/2204.01668/) (2022)
- [Bayesian Lesion Estimation with a Structured Spike-and-Slab Prior](https://mlsanity.com/paper/2206.09175/) (2022)
- [Bayesian Image-on-Scalar Regression with a Spatial Global-Local Spike-and-Slab Prior](https://mlsanity.com/paper/2209.08234/) (2022)

## Built on by (most cited first, 2 of 2)

- [Formal Bayesian Transfer Learning via the Total Risk Prior](https://mlsanity.com/paper/2507.23768/) (2025)
- [Hierarchical Projection for Adaptive Knowledge Transfer](https://mlsanity.com/paper/2606.08691/) (2026)

## Abstract (arXiv)

The popularity of transfer learning stems from the fact that it can borrow information from useful auxiliary datasets. Existing statistical transfer learning methods usually adopt a global similarity measure between the source data and the target data, which may lead to inefficiency when only partial information is shared. In this paper, we propose a novel Bayesian transfer learning method named ``CONCERT'' to allow robust partial information transfer for high-dimensional data analysis. A conditional spike-and-slab prior is introduced in the joint distribution of target and source parameters for information transfer. By incorporating covariate-specific priors, we can characterize partial similarities and integrate source information collaboratively to improve the performance on the target. In contrast to existing work, the CONCERT is a one-step procedure which achieves variable selection and information transfer simultaneously. We establish variable selection consistency, as well as estimation and prediction error bounds for CONCERT. Our theory demonstrates the covariate-specific benefit of transfer learning. To ensure the scalability of the algorithm, we adopt the variational Bayes framework to facilitate implementation. Extensive experiments and two real data applications showcase the validity and advantages of CONCERT over existing cutting-edge transfer learning methods.