Distributed Stochastic Multi-Task Learning with Graph Regularization

Understand

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.