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Ali H. Sayed

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Related publications (142)

Local Tomography of Large Networks Under the Low-Observability Regime

Ali H. Sayed, Augusto José Rabelo Almeida Santos

This article studies the problem of reconstructing the topology of a network of interacting agents via observations of the state-evolution of the agents. We focus on the large-scale network setting with the additional constraint of partial observations, wh ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Affine Combination of Diffusion Strategies Over Networks

Ali H. Sayed, Jie Chen

Diffusion adaptation is a powerful strategy for distributed estimation and learning over networks. Motivated by the concept of combining adaptive filters, this work proposes a combination framework that aggregates the operation of multiple diffusion strate ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Second-Order Guarantees In Federated Learning

Ali H. Sayed, Stefan Vlaski, Elsa Rizk

Federated learning is a useful framework for centralized learning from distributed data under practical considerations of heterogeneity, asynchrony, and privacy. Federated architectures are frequently deployed in deep learning settings, which generally giv ...

IEEE2020

A Proximal Diffusion Strategy for Multiagent Optimization With Sparse Affine Constraints

Ali H. Sayed, Kun Yuan, Sulaiman A S A E Alghunaim

This article develops a proximal primal-dual decentralized strategy for multiagent optimization problems that involve multiple coupled affine constraints, where each constraint may involve only a subset of the agents. The constraints are generally sparse, ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Learning Graph Influence From Social Interactions

Ali H. Sayed, Augusto José Rabelo Almeida Santos, Virginia Bordignon

In social learning, agents form their opinions or beliefs about certain hypotheses by exchanging local information. This work considers the recent paradigm of weak graphs, where the network is partitioned into sending and receiving components, with the for ...

IEEE2020

Interplay Between Topology and Social Learning Over Weak Graphs

Ali H. Sayed, Augusto José Rabelo Almeida Santos, Virginia Bordignon

This work examines a distributed learning problem where the agents of a network form their beliefs about certain hypotheses of interest. Each agent collects streaming (private) data and updates continually its belief by means of a diffusion strategy, which ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Distributed Coupled Multiagent Stochastic Optimization

Ali H. Sayed, Sulaiman A S A E Alghunaim

This paper develops an effective distributed strategy for the solution of constrained multiagent stochastic optimization problems with coupled parameters across the agents. In this formulation, each agent is influenced by only a subset of the entries of a ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Variance-Reduced Stochastic Learning Under Random Reshuffling

Ali H. Sayed, Bicheng Ying, Kun Yuan

Several useful variance-reduced stochastic gradient algorithms, such as SVRG, SAGA, Finito, and SAG, have been proposed to minimize empirical risks with linear convergence properties to the exact minimizer. The existing convergence results assume uniform d ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Revisiting correlation-based functional connectivity and its relationship with structural connectivity

Dimitri Nestor Alice Van De Ville, Ali H. Sayed, Augusto José Rabelo Almeida Santos, Raphaël Pierre Liégeois

Patterns of brain structural connectivity (SC) and functional connectivity (FC) are known to be related. In SC-FC comparisons, FC has classically been evaluated from correlations between functional time series, and more recently from partial correlations o ...

MIT PRESS2020

Adaptation And Learning In Multi-Task Decision Systems

Ali H. Sayed

Adaptation and learning over multi-agent networks is a topic of great relevance with important implications. Elaborating on previous works on single-task networks engaged in decision problems, here we consider the multi-task version in the challenging scen ...

IEEE2020

Graph Learning Under Partial Observability

Ali H. Sayed, Augusto José Rabelo Almeida Santos

Many optimization, inference, and learning tasks can be accomplished efficiently by means of decentralized processing algorithms where the network topology (i.e., the graph) plays a critical role in enabling the interactions among neighboring nodes. There ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Adaptation and Learning Over Networks Under Subspace Constraints-Part I: Stability Analysis

Ali H. Sayed, Roula Nassif, Stefan Vlaski

This paper considers optimization problems over networks where agents have individual objectives to meet, or individual parameter vectors to estimate, subject to subspace constraints that require the objectives across the network to lie in low-dimensional ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Convex Combination of Diffusion Strategies Over Networks

Ali H. Sayed, Jie Chen

Combining diffusion strategies with complementary properties enables enhanced performance when they can be run simultaneously. In this article, we first propose two schemes for the convex combination of two diffusion strategies, namely, the power-normalize ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Multitask Learning Over Graphs: An Approach for Distributed, Streaming Machine Learning

Ali H. Sayed, Roula Nassif, Stefan Vlaski, Jie Chen

The problem of simultaneously learning several related tasks has received considerable attention in several domains, especially in machine learning, with the so-called multitask learning (MTL) problem, or learning to learn problem [1], [2]. MTL is an appro ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Adaptation and Learning Over Networks Under Subspace Constraints & x2014;Part II: Performance Analysis

Ali H. Sayed, Roula Nassif, Stefan Vlaski

Part & x00A0;I of this paper considered optimization problems over networks where agents have individual objectives to meet, or individual parameter vectors to estimate, subject to subspace constraints that require the objectives across the network to lie ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Second-order guarantees in centralized, federated and decentralized nonconvex optimization

Ali H. Sayed, Stefan Vlaski

Rapid advances in data collection and processing capabilities have allowed for the use of increasingly complex models that give rise to nonconvex optimization problems. These formulations, however, can be arbitrarily difficult to solve in general, in the s ...

INT PRESS BOSTON, INC2020

Dynamic Federated Learning

Ali H. Sayed, Stefan Vlaski, Elsa Rizk

Federated learning has emerged as an umbrella term for centralized coordination strategies in multi-agent environments. While many federated learning architectures process data in an online manner, and are hence adaptive by nature, most performance analyse ...

IEEE2020

Walkman: A Communication-Efficient Random-Walk Algorithm for Decentralized Optimization

Ali H. Sayed, Kun Yuan

This paper introduces a new algorithm for consensus optimization in a multi-agent network, where all agents collaboratively find a minimizer for the sum of their private functions. All decentralized algorithms rely on communications between adjacent nodes. ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Linear convergence of primal-dual gradient methods and their performance in distributed optimization

Ali H. Sayed, Sulaiman A S A E Alghunaim

In this work, we revisit a classical incremental implementation of the primal-descent dual-ascent gradient method used for the solution of equality constrained optimization problems. We provide a short proof that establishes the linear (exponential) conver ...

PERGAMON-ELSEVIER SCIENCE LTD2020

Diffusion LMS With Communication Delays: Stability and Performance Analysis

Ali H. Sayed, Roula Nassif

We study the problem of distributed estimation over adaptive networks where communication delays exist between nodes. In particular, we investigate the diffusion Least-Mean-Square (LMS) strategy where delayed intermediate estimates (due to the communicatio ...

IEEE Institute of Electrical and Electronics Engineers2020

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