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Xiaochuan Zhao
Researcher at University of California, Los Angeles
Publications - 50
Citations - 1836
Xiaochuan Zhao is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Asynchronous communication & Network topology. The author has an hindex of 17, co-authored 49 publications receiving 1598 citations. Previous affiliations of Xiaochuan Zhao include Goldman Sachs & Beijing University of Posts and Telecommunications.
Papers
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Diffusion strategies for adaptation and learning over networks: an examination of distributed strategies and network behavior
TL;DR: It is shown that it is an extraordinary property of biological networks that sophisticated behavior is able to emerge from simple interactions among lower-level agents.
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Diffusion Adaptation Over Networks Under Imperfect Information Exchange and Non-Stationary Data
TL;DR: This paper investigates the mean-square performance of general adaptive diffusion algorithms in the presence of various sources of imperfect information exchanges, quantization errors, and model non-stationarities and reveals that link noise over the regression data modifies the dynamics of the network evolution in a distinct way, and leads to biased estimates in steady-state.
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Performance Limits for Distributed Estimation Over LMS Adaptive Networks
Xiaochuan Zhao,Ali H. Sayed +1 more
TL;DR: This work analyzes the mean-square performance of different strategies for distributed estimation over least-mean-squares (LMS) adaptive networks and establishes that, for sufficiently small step-sizes, diffusion strategies can outperform centralized block or incremental LMS strategies by optimizing over left-stochastic combination weighting matrices.
Posted Content
Exact Diffusion for Distributed Optimization and Learning --- Part I: Algorithm Development
TL;DR: The exact diffusion method is applicable to locally balanced left-stochastic combination matrices which, compared to the conventional doubly stochastic matrix, are more general and able to endow the algorithm with faster convergence rates, more flexible step-size choices, and improved privacy-preserving properties.
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Exact Diffusion for Distributed Optimization and Learning—Part I: Algorithm Development
TL;DR: In this paper, a distributed optimization strategy with guaranteed exact convergence for a broad class of left-stochastic combination policies was developed, which is applicable to locally balanced combination matrices which are more general and able to endow the algorithm with faster convergence rates, more flexible step-size choices, and improved privacy-preserving properties.