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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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Proceedings ArticleDOI
Distributed processing over adaptive networks
Ali H. Sayed,Cassio G. Lopes +1 more
TL;DR: In this article, the authors provide an overview of recent work on distributed adaptive algorithms focusing mainly on incremental and diffusion strategies and comments on the mean-square-error performance of the incremental solution.
Journal ArticleDOI
Multitask Diffusion Adaptation Over Networks With Common Latent Representations
TL;DR: This paper examines an alternative way to model relations among tasks by assuming that they all share a common latent feature representation and presents a new multitask learning formulation and algorithms developed for its solution in a distributed online manner.
Journal ArticleDOI
Robust FxLMS algorithms with improved convergence performance
Markus Rupp,Ali H. Sayed +1 more
TL;DR: This paper proposes two modifications of the filtered-x least mean squares algorithm with improved convergence behavior albeit at the same computational cost of 2M operations per time step as the original FxLMS update.
Posted Content
Exact Diffusion for Distributed Optimization and Learning --- Part II: Convergence Analysis
TL;DR: The exact diffusion algorithm developed to remove the bias that is characteristic of distributed solutions for deterministic optimization problems has a wider stability range than the EXTRA consensus solution, meaning that it is stable for a wider range of step-sizes and can attain faster convergence rates.
Journal ArticleDOI
Steady-State Performance Analysis of a Variable Tap-Length LMS Algorithm
TL;DR: A steady-state performance analysis of the fractional tap-length (FT) variableTap-length least mean square (LMS) algorithm gives insight into the performance of the FT algorithm, which may potentially extend its practical applicability.