Covariate-Elaborated Robust Partial Information Transfer with Conditional Spike-and-Slab Prior
This addresses a bottleneck in transfer learning for high-dimensional data analysis by enabling robust partial information transfer, which is incremental but offers specific gains over existing methods.
The paper tackles the problem of inefficient transfer learning when only partial information is shared between source and target datasets by proposing CONCERT, a Bayesian method that uses a conditional spike-and-slab prior for covariate-specific information transfer, achieving improved performance in high-dimensional data analysis.
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 that our algorithm is scalable, we adopt the variational Bayes framework to facilitate implementation. Extensive experiments and two real data applications showcase the validity and advantage of CONCERT over existing cutting-edge transfer learning methods.