ICML 2017poster196 citations
Efficient Distributed Learning with Sparsity
Jialei Wang, Mladen Kolar, Nathan Srebro, Tong Zhang
Abstract
We propose a novel, efficient approach for distributed sparse learning with observations randomly partitioned across machines. In each round of the proposed method, worker machines compute the gradient of the loss on local data and the master machine solves a shifted $\ell_1$ regularized loss minimization problem. After a number of communication rounds that scales only logarithmically with the number of machines, and independent of other parameters of the problem, the proposed approach provably matches the estimation error bound of centralized methods.
BibTeX
@InProceedings{pmlr-v70-wang17f,
title = {Efficient Distributed Learning with Sparsity},
author = {Jialei Wang and Mladen Kolar and Nathan Srebro and Tong Zhang},
booktitle = {Proceedings of the 34th International Conference on Machine Learning},
pages = {3636--3645},
year = {2017},
editor = {Precup, Doina and Teh, Yee Whye},
volume = {70},
series = {Proceedings of Machine Learning Research},
month = {06--11 Aug},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v70/wang17f/wang17f.pdf},
url = {https://proceedings.mlr.press/v70/wang17f.html},
abstract = {We propose a novel, efficient approach for distributed sparse learning with observations randomly partitioned across machines. In each round of the proposed method, worker machines compute the gradient of the loss on local data and the master machine solves a shifted $\ell_1$ regularized loss minimization problem. After a number of communication rounds that scales only logarithmically with the number of machines, and independent of other parameters of the problem, the proposed approach provably matches the estimation error bound of centralized methods.}
}