Distributed Multi-Task Learning with Shared Representation

Jialei Wang; Mladen Kolar; Nathan Srebro

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arXiv · 1603.02185

Distributed Multi-Task Learning with Shared Representation

Jialei Wang·Mladen Kolar·Nathan Srebro

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Abstract

We study the problem of distributed multi-task learning with shared representation, where each machine aims to learn a separate, but related, task in an unknown shared low-dimensional subspaces, i.e. when the predictor matrix has low rank. We consider a setting where each task is handled by a different machine, with samples for the task available locally on the machine, and study communication-efficient methods for exploiting the shared structure.

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Jialei Wang, Mladen Kolar, Nathan Srebro. 2016-03-07. Distributed Multi-Task Learning with Shared Representation. https://arxiv.org/abs/1603.02185

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