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Ali H. Sayed
Researcher at École Polytechnique Fédérale de Lausanne
Publications - 766
Citations - 39568
Ali H. Sayed is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Adaptive filter & Optimization problem. The author has an hindex of 81, co-authored 728 publications receiving 36030 citations. Previous affiliations of Ali H. Sayed include Harbin Engineering University & University of California, Los Angeles.
Papers
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On the Performance of Exact Diffusion over Adaptive Networks
TL;DR: It is shown that the correction step in exact diffusion can lead to better steady-state performance than traditional methods, and this paper provides affirmative results.
Proceedings ArticleDOI
An unbiased and cost-effective leaky-LMS filter
Vitor H. Nascimento,Ali H. Sayed +1 more
TL;DR: In this article, a modified leaky-LMS filter is proposed to ensure stability of the estimates w(k) in the presence of bounded noise, without introducing any bias term and with the added cost of only a comparison and a multiplication per iteration when compared to the classical LMS algorithm.
Posted Content
Social Learning over Weakly-Connected Graphs
TL;DR: In this paper, the authors study diffusion social learning over weakly-connected graphs and show that the asymmetric flow of information hinders the learning abilities of certain agents regardless of their local observations.
Proceedings ArticleDOI
A fast iterative solution for worst-case parameter estimation with bounded model uncertainties
TL;DR: In this paper, the problem of worst-case parameter estimation in the presence of bounded uncertainties in a linear regression model has been formulated and solved in Chandrasekaran et al. (1997).
Journal ArticleDOI
Structured matrices and fast RLS adaptive filtering
Ali H. Sayed,Thomas Kailath +1 more
TL;DR: The recursions are extended to a class of structured time-variant state-space models, and connections with the Schur algorithm are discussed, and a transparent derivation of fast recursive least-squares algorithms is given.