Latent Group Structured Multi-task Learning

TL;DR

This paper introduces a group-structured latent space model for multi-task learning that leverages prior information to improve task relationship modeling, demonstrating competitive results on synthetic and real datasets.

Contribution

It proposes a novel group-structured latent space approach for multi-task learning that incorporates prior information and uses alternating minimization for training.

Findings

Competitive performance over single-task learning

Effective modeling of task groups based on prior information

Validated on synthetic and real-world datasets

Abstract

In multi-task learning (MTL), we improve the performance of key machine learning algorithms by training various tasks jointly. When the number of tasks is large, modeling task structure can further refine the task relationship model. For example, often tasks can be grouped based on metadata, or via simple preprocessing steps like K-means. In this paper, we present our group structured latent-space multi-task learning model, which encourages group structured tasks defined by prior information. We use an alternating minimization method to learn the model parameters. Experiments are conducted on both synthetic and real-world datasets, showing competitive performance over single-task learning (where each group is trained separately) and other MTL baselines.