Learning Task Grouping and Overlap in Multi-task Learning
This work addresses the challenge of optimizing task grouping and overlap in multi-task learning, which is incremental as it builds on existing paradigms with a novel modeling approach.
The paper tackles the problem of selectively sharing information across tasks in multi-task learning by modeling each task parameter vector as a sparse linear combination of underlying basis tasks, allowing tasks in different groups to overlap. Experimental results on four datasets show that this approach outperforms competing methods.
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. Our model is based on on the assumption that task pa- rameters within a group lie in a low dimen- sional subspace but allows the tasks in differ- ent groups to overlap with each other in one or more bases. Experimental results on four datasets show that our approach outperforms competing methods.