Projection-free Distributed Online Learning in Networks

By Wenpeng Zhang, Peilin Zhao, Wenwu Zhu, Steven C. H. Hoi, T. Zhang. 2017

96 citations recorded by the source corpus.

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Citations in the corpus

Decentralized Online Learning: Take Benefits from Others’ Data without Sharing Your Own to Track Global Trend (2019, methodology): “…learning algorithms has been widely studied for decades [Zinkevich, 2003, Hall and Willett, 2015, 2013, Jadbabaie et al., 2015, Yang et al., 2016, Bedi et al., 2018, Zhang et al., 2017a, Mokhtari et al., 2016, Zhang et al., 2018b, Gy¨orgy and Szepesv´ari, 2016, Wei et al., 2016, Zhao et al., 2018].”
D2: Decentralized Training over Decentralized Data (2018, background): “The decentralized stochastic gradient descent (Lian et al., 2017a; Zhang et al., 2017; Shahrampour & Jadbabaie, 2017) allows each worker (say worker i) maintaining its own local variable x.”
Communication Compression for Decentralized Training (2018, methodology): “The commonly used stochastic unbiased compression include random quantization 1 [Zhang et al., 2017a] and sparsiﬁcation 2 [Wangni et al., 2017, Konečn`y and Richtárik, 2016].”
Asynchronous Decentralized Online Learning (2021, methodology): “We conduct the comparison on three representative types of graphs with varying connectivities [8, 31], i.”
The Frank-Wolfe Algorithm: A Short Introduction (2023, unknown): “…and Vondrák [5], Mirzasoleiman et al. [60], Hassani et al. [45], Mokhtari et al. [61], Anari et al. [1], Anari et al. [2], Mokhtari et al. [62], Bach [4]), online learning (see, e.g., Hazan and Kale [46], Zhang et al. [86], Chen et al. [20], Garber and Kretzu [39], Kerdreux et al. [51],…”
Proximal Online Gradient is Optimum for Dynamic Regret (2018, background): “Online learning [Zinkevich, 2003, Shalev-Shwartz, 2012, Hazan, 2016, Mohri and Yang, 2016, Zhang et al., 2018, Jun et al., 2017, Jain et al., 2012, Zhang et al., 2017b] is a hot research topic for the last decade of years, due to its application in practices such as online recommendation [Chaudhuri…”
Decentralization Meets Quantization (2018, methodology): “To reduce the communication cost, a straightforward idea is to let nodes exchange low precision x(i) (e.g., one can use unbiased random quantization1 [Zhang et al., 2017a] or sparsification2 [Wangni et al., 2017] to compress local x(i) before exchanging).”
Online Learning: A Comprehensive Survey (2018, background): “Distributed online learning (Zhang et al., 2017) has become increasing popular due to the explosion in size and complexity of datasets.”