We consider the problem of distributed multitask
learning, where each machine learns a separate,
but related, task. Specifically, each machine
learns a linear predictor in high-dimensional
space, where all tasks share the same small support.
We present a communication-efficient estimator
based on the debiased lasso and show
that it is comparable with the optimal centralized
method.
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Distributed Multi-Task Learning with Shared Representation
We study the problem of distributed multitask 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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- Award ID(s):
- 1302662
- PAR ID:
- 10025959
- Date Published:
- Journal Name:
- arXiv.org
- ISSN:
- 2331-8422
- Page Range / eLocation ID:
- arXiv:1603.02185v1 [cs.LG]
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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