Learning an Interpretable Graph Structure in Multi-Task Learning
This addresses the challenge of multi-task learning where task relationships are often unknown or pre-estimated, offering a more integrated and interpretable approach for predictive modeling applications.
The paper tackles the problem of jointly learning multiple tasks and their relationships by proposing a method that simultaneously learns an interpretable, sparse graph structure and model parameters, reducing generalization error and revealing easier-to-interpret task relationships compared to six state-of-the-art methods.
We present a novel methodology to jointly perform multi-task learning and infer intrinsic relationship among tasks by an interpretable and sparse graph. Unlike existing multi-task learning methodologies, the graph structure is not assumed to be known a priori or estimated separately in a preprocessing step. Instead, our graph is learned simultaneously with model parameters of each task, thus it reflects the critical relationship among tasks in the specific prediction problem. We characterize graph structure with its weighted adjacency matrix and show that the overall objective can be optimized alternatively until convergence. We also show that our methodology can be simply extended to a nonlinear form by being embedded into a multi-head radial basis function network (RBFN). Extensive experiments, against six state-of-the-art methodologies, on both synthetic data and real-world applications suggest that our methodology is able to reduce generalization error, and, at the same time, reveal a sparse graph over tasks that is much easier to interpret.