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
Posted Content
Graph Learning over Partially Observed Diffusion Networks: Role of Degree Concentration
TL;DR: The analysis reveals that the fundamental property enabling consistent graph learning is the statistical concentration of node degrees, and this claim is proved for three matrix estimators.
Journal ArticleDOI
Digital Suppression of Spurious PLL Tones in A/D Converters
Shang-Kee Ting,Ali H. Sayed +1 more
TL;DR: This paper proposes estimation and filtering techniques in the digital domain to clean the data and remove the PLL leakage effects by using signal processing compensation algorithms and studies the performance of the proposed estimation algorithms and compared with the corresponding Cramer-Rao bound.
Proceedings ArticleDOI
Attaining optimal batch performance via distributed processing over networks
Xiaochuan Zhao,Ali H. Sayed +1 more
TL;DR: This work shows how the combination weights of diffusion strategies for adaptation and learning over networks can be chosen in order for the network mean-square-error performance to match that of an optimized centralized (or batch) solution.
Proceedings ArticleDOI
Distributed Inference Over Multitask Graphs Under Smoothness
TL;DR: This paper formulates a multitask optimization problem where agents in the network have individual objectives to meet, or individual parameter vectors to estimate, subject to a smoothness condition over the graph.
Journal ArticleDOI
Graph Learning Under Partial Observability
TL;DR: In this article, the authors examine the inverse problem and consider the reverse question: How much information does observing the behavior at the nodes of a graph convey about the underlying topology? For large-scale networks, the difficulty in addressing such inverse problems is compounded by the fact that usually only a limited fraction of the nodes can be probed.