Abstract
This paper introduces self-paced task selection
to multitask learning, where instances from more
closely related tasks are selected in a progression of
easier-to-harder tasks, to emulate an effective human
education strategy, but applied to multitask machine
learning. We develop the mathematical foundation
for the approach based on iterative selection of the
most appropriate task, learning the task parameters,
and updating the shared knowledge, optimizing a
new bi-convex loss function. This proposed method
applies quite generally, including to multitask feature learning, multitask learning with alternating
structure optimization, etc. Results show that in
each of the above formulations self-paced (easierto-harder) task selection outperforms the baseline
version of these methods in all the experiments