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Learning over social networks via diffusion adaptation

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Distributed Learning over Networks under Subspace Constraints

Ali H. Sayed, Stefan Vlaski, Roula Nassif

This work presents and studies a distributed algorithm for solving optimization problems over networks where agents have individual costs to minimize subject to subspace constraints that require the minimizers across the network to lie in a low-dimensional ...

IEEE2019

A review of robot rescue simulation platforms for robotics education

Josephine Anna Eleanor Hughes

This review explores a natural learning curve which gives an appropriate RoboCup Rescue challenge at the right age. Children who got involved in the age group 14+ should continue their learning experience until they reach graduate level. To reduce the cost ...

Springer2019

Computational characteristics and hardware implications of brain tissue simulations

Francesco Cremonesi

Understanding the link between the brain's anatomy and its function through computer simulations of neural tissue models is a widely used approach in computational neuroscience. This technique enables rapid prototyping and testing of hypotheses, allowing r ...

EPFL2019

Social Learning Over Weakly Connected Graphs

Ali H. Sayed, Bicheng Ying

In this paper, we study diffusion social learning over weakly connected graphs. We show that the asymmetric flow of information hinders the learning abilities of certain agents regardless of their local observations. Under some circumstances that we clarif ...

2017

Learning by networked agents under partial information

Ali H. Sayed

In many scenarios of interest, agents may only have access to partial information about an unknown model or target vector. Each agent may be sensing only a subset of the entries of a global target vector, and the number of these entries can be different ac ...

IEEE2017

Diffusion social learning over weakly-connected graphs

Ali H. Sayed, Bicheng Ying

In this paper, we study diffusion social learning over weakly-connected graphs. We show that the asymmetric flow of information hinders the learning abilities of certain agents regardless of their local observations. Under some circumstances that we clarif ...

IEEE2016

Blob dynamics in the TORPEX experiment: a multi-code validation

Ivo Furno, Paolo Ricci, Christian Gabriel Theiler, Fabio Riva, Eric Serre

Three-dimensional and two-dimensional seeded blob simulations are performed with five different fluid models, all based on the drift-reduced Braginskii equations, and the numerical results are compared among themselves and validated against experimental me ...

Institute of Physics2016

On the Learning Behavior of Adaptive Networks - Part II: Performance Analysis

Ali H. Sayed

Part I of this paper examined the mean-square stability and convergence of the learning process of distributed strategies over graphs. The results identified conditions on the network topology, utilities, and data in order to ensure stability; the results ...

IEEE2015

Information-Sharing Over Adaptive Networks With Self-Interested Agents

Ali H. Sayed

We examine the behavior of multiagent networks where information-sharing is subject to a positive communications cost over the edges linking the agents. We consider a general mean-square-error formulation, where all agents are interested in estimating the ...

IEEE2015

Diffusion adaptation over networks

Ali H. Sayed

Adaptive networks are well-suited to perform decentralized information processing and optimization tasks and to model various types of self-organized and complex behavior encountered in nature. Adaptive networks consist of a collection of agents with proce ...

Elsevier2014