Learning Task Grouping and Overlap in Multi-task Learning

Understand

In the paradigm of multi-task learning, mul- tiple related prediction tasks are learned jointly, sharing information across the tasks.

• We propose a framework for multi-task learn- ing that enables one to selectively share the information across the tasks.
• We assume that each task parameter vector is a linear combi- nation of a finite number of underlying basis tasks.
• The coefficients of the linear combina- tion are sparse in nature and the overlap in the sparsity patterns of two tasks controls the amount of sharing across these.