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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
More filters
Proceedings ArticleDOI
Combination weights for diffusion strategies with imperfect information exchange
Xiaochuan Zhao,Ali H. Sayed +1 more
TL;DR: This paper investigates the mean-square performance of adaptive diffusion algorithms in the presence of various sources of imperfect information exchanges and quantization errors, and reveals that link noise over the regression data modifies the dynamics of the network evolution, and leads to biased estimates in steady-state.
Proceedings ArticleDOI
Tomography of Adaptive Multi-Agent Networks Under Limited Observation
Vincenzo Matta,Ali H. Sayed +1 more
TL;DR: This work establishes, under reasonable conditions, that consistent tomography is possible, namely, that it is possible to reconstruct the interaction profile of the observable portion of the network, with negligible error as the network size increases.
Proceedings ArticleDOI
On the generalization ability of distributed online learners
TL;DR: A fully-distributed stochastic-gradient strategy based on diffusion adaptation techniques is proposed, which shows that, for strongly convex risk functions, the excess-risk at every node decays at the rate of O(1/Ni), where N is the number of learners and i is the iteration index.
Proceedings ArticleDOI
Decentralized exact coupled optimization
TL;DR: This work develops an exact converging algorithm for the solution of a distributed optimization problem with partially-coupled parameters across agents in a multi-agent scenario that is shown to converge to the true optimizer at a linear rate for strongly-convex cost functions.
Proceedings ArticleDOI
Divide-and-conquer tomography for large-scale networks
TL;DR: This work considers the problem of reconstructing the topology of a network of interacting agents via observations of the state-evolution of the agents, and explores the possibility of reconstructioning a larger network via repeated application of the local tomography algorithm to smaller network portions.