Learning Task Grouping using Supervised Task Space Partitioning in Lifelong Multitask Learning

Abstract

Lifelong multitask learning is a multitask learning framework in which a learning agent faces the tasks that need to be learnt in an online manner. Lifelong multitask learning framework may be applied to a variety of applications such as image annotation, robotics, automated machines etc, and hence, may prove to be a highly promising direction for further investigation. However, the lifelong learning framework comes with its own baggage of challenges. The biggest challenge is the fact that the characteristics of the future tasks which might be encountered by the learning agents are entirely unknown. If all the tasks are assumed to be related, there may be a risk of training from unrelated task resulting in negative transfer of information. To overcome this problem, both batch and online multitask learning algorithms learn task relationships. However, due to the unknown nature of the future tasks, learning the task relationships is also difficult in lifelong multitask learning. In this paper, we propose learning functions to model the task relationships as it is computationally cheaper in an online setting. More specifically, we learn partition functions in the task space to divide the tasks into cluster. Our major contribution is to present a global formulation to learn both the task partitions and the parameters. We provide a supervised learning framework to estimate both the partition function and the model. The current method has been implemented and compared against other leading lifelong learning algorithms using several real world datasets, and we show that the current method has a superior performance.