A
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
More filters
Proceedings ArticleDOI
Convergence of Variance-Reduced Learning Under Random Reshuffling
TL;DR: This paper provides the first theoretical guarantee of linear convergence under random reshuffling for SAGA and proposes a new amortized variance-reduced gradient (AVRG) algorithm with constant storage requirements compared to SAG a and with balanced gradient computations compared toSVRG.
Proceedings ArticleDOI
Adaptive frequency-domain equalization of space-time block-coded transmissions
TL;DR: An adaptive equalization scheme for space-time block-coded (STBC) transmissions is developed based on a modified low-complexity version of the fast-converging RLS algorithm, achieving complexity reduction by exploiting the rich structure of STBC.
Journal ArticleDOI
Adaptive Social Learning
TL;DR: In this paper, the authors proposed an adaptive social learning (ASL) strategy, which relies on a small step-size parameter to tune the adaptation degree, and analyzed the performance of this strategy under standard global identifiability assumptions.
Journal ArticleDOI
Special Issue on Structured and Infinite Systems of Linear Equations
TL;DR: In this article, a special issue on Structured and Infinite Systems of Linear Equations is presented, where the authors discuss the structural and infinite systems of linear equations and their applications.
Proceedings ArticleDOI
Consistent Tomography over Diffusion Networks under the Low-Observability Regime
TL;DR: This work considers a diffusion network responding to streaming data, and studies the problem of identifying the topology of a subnetwork of observable agents by tracking their output measurements.