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Creators/Authors contains: "Liu, Xiaorui"

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  1. ; ; ; ; ; (, ACM)
  2. ; ; ; ; ; ; ; ; ; (, In Proceedings of the 39th IEEE International Conference on Data Engineering (ICDE 2023))
    Full Text Available 
  3. ; ; ; (, IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm))
    Full Text Available 
  4. ; ; ; ; (, ICLR 2021)
    Communication compression has become a key strategy to speed up distributed optimization. However, existing decentralized algorithms with compression mainly focus on compressing DGD-type algorithms. They are unsatisfactory in terms of convergence rate, stability, and the capability to handle heterogeneous data. Motivated by primal-dual algorithms, this paper proposes the first \underline{L}in\underline{EA}r convergent \underline{D}ecentralized algorithm with compression, LEAD. Our theory describes the coupled dynamics of the inexact primal and dual update as well as compression error, and we provide the first consensus error bound in such settings without assuming bounded gradients. Experiments on convex problems validate our theoretical analysis, and empirical study on deep neural nets shows that LEAD is applicable to non-convex problems. 
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    Accepted Manuscript Full Text Available
  5. ; ; ; ; ; (, Chemistry of Materials)
    null (Ed.)
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  6. ; ; ; ; ; (, 2020 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining)
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  7. ; ; ; ; ; ; ; ; ; ; et al (, TWC 2022)
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