Distributed Stochastic Multi-Task Learning with Graph Regularization

02/11/2018
by   Weiran Wang, et al.
0

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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset