Pareto Multi-Task Learning

Understand

Multi-task learning is a powerful method for solving multiple correlated tasks simultaneously.

• However, it is often impossible to find one single solution to optimize all the tasks, since different tasks might conflict with each other.
• Recently, a novel method is proposed to find one single Pareto optimal solution with good trade-off among different tasks by casting multi-task learning as multiobjective optimization.
• In this paper, we generalize this idea and propose a novel Pareto multi-task learning algorithm (Pareto MTL) to find a set of well-distributed Pareto solutions which can represent different trade-offs among different tasks.