# Learning task structure via sparsity grouped multitask learning

## TL;DR

This paper introduces a joint optimization approach to recover task group structures and sparsity patterns in high-dimensional multitask learning, improving feature selection accuracy.

## Contribution

It proposes a novel regularizer-based joint optimization framework for simultaneously recovering task groups and sparsity patterns in multitask learning.

## Key findings

- Accurately recovers task group structures in experiments.
- Enhances feature selection in high-dimensional settings.
- Demonstrates effectiveness through extensive empirical validation.

## Abstract

Sparse mapping has been a key methodology in many high-dimensional scientific problems. When multiple tasks share the set of relevant features, learning them jointly in a group drastically improves the quality of relevant feature selection. However, in practice this technique is used limitedly since such grouping information is usually hidden. In this paper, our goal is to recover the group structure on the sparsity patterns and leverage that information in the sparse learning. Toward this, we formulate a joint optimization problem in the task parameter and the group membership, by constructing an appropriate regularizer to encourage sparse learning as well as correct recovery of task groups. We further demonstrate that our proposed method recovers groups and the sparsity patterns in the task parameters accurately by extensive experiments.