# Multi-Task Learning Regression via Convex Clustering

arXiv: https://arxiv.org/abs/2304.13342 · HTML: https://mlsanity.com/paper/2304.13342/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 26 Apr 2023.

## Builds on (most cited first, 7 of 7)

- [Network Lasso: Clustering and Optimization in Large Graphs](https://mlsanity.com/paper/1507.00280/) (2015)
- [Convex Clustering: Model, Theoretical Guarantee and Efficient Algorithm](https://mlsanity.com/paper/1810.02677/) (2018)
- [Statistical Properties of Convex Clustering](https://mlsanity.com/paper/1503.08340/) (2015)
- [Localized Lasso for High-Dimensional Regression](https://mlsanity.com/paper/1603.06743/) (2016)
- [Efficient and Scalable Multi-task Regression on Massive Number of Tasks](https://mlsanity.com/paper/1811.05695/) (2018)
- [Converting ADMM to a Proximal Gradient for Efficient Sparse Estimation](https://mlsanity.com/paper/2104.10911/) (2021)
- [A Bayesian approach to multi-task learning with network lasso](https://mlsanity.com/paper/2110.09040/) (2021)

## Built on by (most cited first, 3 of 3)

- [Multi-task learning via robust regularized clustering with non-convex group penalties](https://mlsanity.com/paper/2404.03250/) (2024)
- [Adaptive Estimation and Inference in Semi-parametric Heterogeneous Clustered Multitask Learning via Neyman Orthogonality](https://mlsanity.com/paper/2605.01907/) (2026)
- [Multitask Regression with Pairwise Fusion](https://mlsanity.com/paper/2609.27280/) (2026)

## Abstract (arXiv)

Multi-task learning (MTL) is a methodology that aims to improve the general performance of estimation and prediction by sharing common information among related tasks. In the MTL, there are several assumptions for the relationships and methods to incorporate them. One of the natural assumptions in the practical situation is that tasks are classified into some clusters with their characteristics. For this assumption, the group fused regularization approach performs clustering of the tasks by shrinking the difference among tasks. This enables us to transfer common information within the same cluster. However, this approach also transfers the information between different clusters, which worsens the estimation and prediction. To overcome this problem, we propose an MTL method with a centroid parameter representing a cluster center of the task. Because this model separates parameters into the parameters for regression and the parameters for clustering, we can improve estimation and prediction accuracy for regression coefficient vectors. We show the effectiveness of the proposed method through Monte Carlo simulations and applications to real data.