UAI 2021poster9 citations
LocalNewton: Reducing communication rounds for distributed learning
Vipul Gupta, Avishek Ghosh, Michał Dereziński, Rajiv Khanna, Kannan Ramchandran, Michael W. Mahoney
Abstract
To address the communication bottleneck problem in distributed optimization within a master-worker framework, we propose LocalNewton, a distributed second-order algorithm with
BibTeX
@InProceedings{pmlr-v161-gupta21a,
title = {LocalNewton: Reducing communication rounds for distributed learning},
author = {Gupta, Vipul and Ghosh, Avishek and Derezi\'nski, Micha{\l} and Khanna, Rajiv and Ramchandran, Kannan and Mahoney, Michael W.},
booktitle = {Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence},
pages = {632--642},
year = {2021},
editor = {de Campos, Cassio and Maathuis, Marloes H.},
volume = {161},
series = {Proceedings of Machine Learning Research},
month = {27--30 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v161/gupta21a/gupta21a.pdf},
url = {https://proceedings.mlr.press/v161/gupta21a.html},
abstract = {To address the communication bottleneck problem in distributed optimization within a master-worker framework, we propose LocalNewton, a distributed second-order algorithm with