  Self-paced learning (SPL) mimics the cognitive process of humans, who
generally learn from easy samples to hard ones. One key issue in SPL is the
training process required for each instance weight depends on the other samples
and thus cannot easily be run in a distributed manner in a large-scale dataset.
In this paper, we reformulate the self-paced learning problem into a
distributed setting and propose a novel Distributed Self-Paced Learning method
(DSPL) to handle large-scale datasets. Specifically, both the model and
instance weights can be optimized in parallel for each batch based on a
consensus alternating direction method of multipliers. We also prove the
convergence of our algorithm under mild conditions. Extensive experiments on
both synthetic and real datasets demonstrate that our approach is superior to
those of existing methods.
