ICML 2020poster20 citations
Simultaneous Inference for Massive Data: Distributed Bootstrap
Yang Yu, Shih-Kang Chao, Guang Cheng
Abstract
In this paper, we propose a bootstrap method applied to massive data processed distributedly in a large number of machines. This new method is computationally efficient in that we bootstrap on the master machine without over-resampling, typically required by existing methods (Kleiner et al., 2014; Sengupta et al., 2016), while provably achieving optimal statistical efficiency with minimal communication. Our method does not require repeatedly re-fitting the model but only applies multiplier bootstrap in the master machine on the gradients received from the worker machines. Simulations validate our theory.
BibTeX
@InProceedings{pmlr-v119-yu20a,
title = {Simultaneous Inference for Massive Data: Distributed Bootstrap},
author = {Yu, Yang and Chao, Shih-Kang and Cheng, Guang},
booktitle = {Proceedings of the 37th International Conference on Machine Learning},
pages = {10892--10901},
year = {2020},
editor = {III, Hal Daumé and Singh, Aarti},
volume = {119},
series = {Proceedings of Machine Learning Research},
month = {13--18 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v119/yu20a/yu20a.pdf},
url = {https://proceedings.mlr.press/v119/yu20a.html},
abstract = {In this paper, we propose a bootstrap method applied to massive data processed distributedly in a large number of machines. This new method is computationally efficient in that we bootstrap on the master machine without over-resampling, typically required by existing methods (Kleiner et al., 2014; Sengupta et al., 2016), while provably achieving optimal statistical efficiency with minimal communication. Our method does not require repeatedly re-fitting the model but only applies multiplier bootstrap in the master machine on the gradients received from the worker machines. Simulations validate our theory.}
}