Communication-Efficient Distributed Learning of Discrete Distributions

By Ilias Diakonikolas, Elena Grigorescu, Jerry Zheng Li, Abhiram Natarajan, Krzysztof Onak, Ludwig Schmidt. 2017

34 citations recorded by the source corpus.

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Citations in the corpus

Inference Under Information Constraints I: Lower Bounds From Chi-Square Contraction (2018, background): “constraints has been studied in [20],6 and, subsequent to an earlier version of this work, the distribution testing problem has been considered in [19].”
Geometric Lower Bounds for Distributed Parameter Estimation Under Communication Constraints (2018, methodology): “(2018), which improves over Diakonikolas et al. (2017). Corollary 6 recover the results in Zhang et al. (2013); Garg et al. (2014) (without logarithmic factors in the risk) under a mild technical condition k ≥ log d.”
Inference Under Information Constraints II: Communication Constraints and Shared Randomness (2019, background): “Among these, our work is closest to the results in [32], [33] and [22].”
Communication and Memory Efficient Testing of Discrete Distributions (2019, background): “…and Valiant (2014); Diakonikolas et al. (2015a); Acharya et al. (2015); Canonne et al. (2016); Diakonikolas and Kane (2016); Diakonikolas et al. (2016); Canonne et al. (2018); Goldreich (2017); Diakonikolas et al. (2017a); Batu and Canonne (2017); Diakonikolas et al. (2018); Canonne et al. (2017b).”
One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Them (2019, background): “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).”
Think Locally, Act Globally: Federated Learning with Local and Global Representations (2020, background): “Recent work has improved the communication efﬁciency of distributed learning by sparsifying the data and model [76], developing efﬁcient gradient-based methods [77, 21], and compressing the updates [75, 83, 49, 52, 46].”
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].”
Distributed Statistical Estimation of High-Dimensional and Nonparametric Distributions (2018, background): “A more recent work [12] studied the same discrete distribution estimation problem, and obtained Corollary 2.”