# A Bayesian approach to multi-task learning with network lasso

arXiv: https://arxiv.org/abs/2110.09040 · HTML: https://mlsanity.com/paper/2110.09040/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 18 Oct 2021.

## Builds on (most cited first, 5 of 5)

- [Network Lasso: Clustering and Optimization in Large Graphs](https://mlsanity.com/paper/1507.00280/) (2015)
- [Localized Lasso for High-Dimensional Regression](https://mlsanity.com/paper/1603.06743/) (2016)
- [On the Duality between Network Flows and Network Lasso](https://mlsanity.com/paper/1910.01805/) (2019)
- [Robust and scalable Bayesian analysis of spatial neural tuning function data](https://mlsanity.com/paper/1606.07845/) (2016)
- [Classifying Partially Labeled Networked Data via Logistic Network Lasso](https://mlsanity.com/paper/1903.10926/) (2019)

## Built on by (most cited first, 1 of 1)

- [Multi-Task Learning Regression via Convex Clustering](https://mlsanity.com/paper/2304.13342/) (2023)

## Abstract (arXiv)

Network lasso is a method for solving a multi-task learning problem through the regularized maximum likelihood method. A characteristic of network lasso is setting a different model for each sample. The relationships among the models are represented by relational coefficients. A crucial issue in network lasso is to provide appropriate values for these relational coefficients. In this paper, we propose a Bayesian approach to solve multi-task learning problems by network lasso. This approach allows us to objectively determine the relational coefficients by Bayesian estimation. The effectiveness of the proposed method is shown in a simulation study and a real data analysis.