  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.
