# Bayesian Transfer Learning

**Authors:** Piotr M. Suder, Jason Xu, David B. Dunson  
**arXiv:** [2312.13484](https://arxiv.org/abs/2312.13484)  
**Date:** 2023-12-20  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2312.13484v1/

## Key points

- Transfer learning aims to improve inference and predictive accuracy in a specific domain by utilizing data from related domains
- Bayesian approaches to transfer learning have received relatively limited attention
- The article presents a comprehensive survey of Bayesian transfer learning frameworks
- One central question addressed is how these methods identify the optimal information to transfer between domains
- The authors provide a simulation study comparing the performance of Bayesian transfer learning methods against frequentist competitors
- The article explores the potential applications of Bayesian transfer learning in improving inference and predictive accuracy across different domains.

## Summary

The article "Bayesian Transfer Learning" by Piotr M. Suder, Jason Xu, and David B. Dunson delves into the concept of transfer learning in statistical machine learning. Transfer learning aims to improve inference and predictive accuracy in a specific domain by utilizing data from related domains. While it has gained recent attention, its foundational principles have existed for years in various forms. Previous literature reviews in computer science and electrical engineering have focused on general methodologies and works within these disciplines. This article specifically highlights Bayesian approaches to transfer learning, which have received relatively limited attention despite their compatibility with leveraging prior knowledge to guide new learning tasks. The authors present a comprehensive survey of Bayesian transfer learning frameworks that can be applied to different practical settings. One central question addressed in this survey is how these methods identify the optimal information to transfer between domains. The authors also provide a simulation study comparing the performance of Bayesian transfer learning methods against frequentist competitors. Overall, this article provides an extensive exploration of Bayesian transfer learning and its potential applications in improving inference and predictive accuracy across different domains.