  Multitask learning has shown promising performance in many applications and
many multitask models have been proposed. In order to identify an effective
multitask model for a given multitask problem, we propose a learning framework
called learning to multitask (L2MT). To achieve the goal, L2MT exploits
historical multitask experience which is organized as a training set consists
of several tuples, each of which contains a multitask problem with multiple
tasks, a multitask model, and the relative test error. Based on such training
set, L2MT first uses a proposed layerwise graph neural network to learn task
embeddings for all the tasks in a multitask problem and then learns an
estimation function to estimate the relative test error based on task
embeddings and the representation of the multitask model based on a unified
formulation. Given a new multitask problem, the estimation function is used to
identify a suitable multitask model. Experiments on benchmark datasets show the
effectiveness of the proposed L2MT framework.
