Distributed Stochastic Multi-Task Learning with Graph Regularization
Machine Learning
2018-02-13 v1 Machine Learning
Abstract
We propose methods for distributed graph-based multi-task learning that are based on weighted averaging of messages from other machines. Uniform averaging or diminishing stepsize in these methods would yield consensus (single task) learning. We show how simply skewing the averaging weights or controlling the stepsize allows learning different, but related, tasks on the different machines.
Cite
@article{arxiv.1802.03830,
title = {Distributed Stochastic Multi-Task Learning with Graph Regularization},
author = {Weiran Wang and Jialei Wang and Mladen Kolar and Nathan Srebro},
journal= {arXiv preprint arXiv:1802.03830},
year = {2018}
}