arXiv · 1802.03830
Distributed Stochastic Multi-Task Learning with Graph Regularization
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.
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Weiran Wang, Jialei Wang, Mladen Kolar, Nathan Srebro. 2018-02-11. Distributed Stochastic Multi-Task Learning with Graph Regularization. https://arxiv.org/abs/1802.03830
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