Communication-efficient Sparse Regression

By Jason D. Lee, Qiang Liu, Yuekai Sun, Jonathan E. Taylor. 2017

193 citations recorded by the source corpus.

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Citations in the corpus

Communication Efficient Decentralized Training with Multiple Local Updates (2019, methodology): “1, 2, 4, 6 [15] Jason D Lee, Qiang Liu, Yuekai Sun, and Jonathan E Taylor.”
On the Convergence of FedAvg on Non-IID Data (2019, neutral): “, 2018; Zhang and Lin, 2015) and the one-shot methods (Lee et al., 2017; Lin et al., 2017; Wang, 2019; Zhang et al., 2013; 2015b).”
Gradient Sparsification for Communication-Efficient Distributed Optimization (2017, background): “There are solutions to specific problems like mean estimation [17, 32], component analysis [23], clustering [6], sparse regression [18] and boosting [7].”
Optimal Convergence Rates for Convex Distributed Optimization in Networks (2019, unknown): “Another line of work considers more restrictive assumptions on the optimization problems to obtain fast convergence rates (Lee et al., 2017; Jes´us Arroyo, 2016; Tian and Gu, 2017).”
Order Optimal One-Shot Distributed Learning (2019, background): “…settings is often the communication between machines, and several recent works have focused on designing communication-efﬁcient algorithms for different machine learning applications [Duchi et al., 2012, Braverman et al., 2016, Chang et al., 2017, Diakonikolas et al., 2017, Lee et al., 2017].”
One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Them (2019, methodology): “For learning statistical models in a distributed fashion, several works have focused on designing communication-efﬁcient algorithms for various machine learning applications (Duchi et al., 2012; Braverman et al., 2016; Chang et al., 2017; Diakonikolas et al., 2017; Lee et al., 2017).”
Communication-Efficient Local Decentralized SGD Methods (2019, methodology): “, one-shot aggregation [47, 48, 14, 19, 41], primal-dual algorithms [32, 33, 6] and distributed Newton methods [29, 46, 26, 30, 23].”