# On Better Exploring and Exploiting Task Relationships in Multi-Task Learning: Joint Model and Feature Learning

## TL;DR

This paper introduces a novel multitask learning approach that jointly learns shared parameters and features to better capture task relatedness, improving task performance and measurement accuracy.

## Contribution

It proposes a new strategy for measuring task relatedness by jointly learning shared parameters and features, addressing limitations of previous independent methods.

## Key findings

- The joint model outperforms existing methods in experiments.
- Theoretical bounds demonstrate improved task relatedness measurement.
- The alternating algorithm effectively optimizes the nonconvex objective.

## Abstract

Multitask learning (MTL) aims to learn multiple tasks simultaneously through the interdependence between different tasks. The way to measure the relatedness between tasks is always a popular issue. There are mainly two ways to measure relatedness between tasks: common parameters sharing and common features sharing across different tasks. However, these two types of relatedness are mainly learned independently, leading to a loss of information. In this paper, we propose a new strategy to measure the relatedness that jointly learns shared parameters and shared feature representations. The objective of our proposed method is to transform the features from different tasks into a common feature space in which the tasks are closely related and the shared parameters can be better optimized. We give a detailed introduction to our proposed multitask learning method. Additionally, an alternating algorithm is introduced to optimize the nonconvex objection. A theoretical bound is given to demonstrate that the relatedness between tasks can be better measured by our proposed multitask learning algorithm. We conduct various experiments to verify the superiority of the proposed joint model and feature a multitask learning method.