Publications by Ali H. Sayed | EPFL Graph Search

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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 ...

This work proposes a decentralized architecture, where individual agents aim at solving a classification problem while observing streaming features of different dimensions and arising from possibly different distributions. In the context of social learning ...

This work addresses the problem of learning the topology of a network from the signals emitted by the network nodes. These signals are generated over time through a linear diffusion process, where neighboring nodes exchange messages according to the underl ...

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 ...

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 ...

In this article, we consider decentralized optimization problems where agents have individual cost functions to minimize subject to subspace constraints that require the minimizers across the network to lie in low-dimensional subspaces. This constrained fo ...

This paper analyzes the implementation of least-mean-squares (LMS)-based, adaptive diffusion algorithms over networks in the frequency-domain (FD). We focus on a scenario of noisy links and include a moving-average step for denoising after self-learning to ...

This work proposes a novel strategy for social learning by introducing the critical feature of adaptation. In social learning, several distributed agents update continually their belief about a phenomenon of interest through: i) direct observation of strea ...

This work proposes a new way of combining independently trained classifiers over space and time. Combination over space means that the outputs of spatially distributed classifiers are aggregated. Combination over time means that the classifiers respond to ...

Part I of this paper formulated 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. A diffusion strategy was devised ...

In this paper we study the problem of social learning under multiple true hypotheses and self-interested agents that exchange information over a graph. In this setup, each agent receives data that might be generated from a different hypothesis (or state) t ...

This work develops an effective distributed algorithm for the solution of stochastic optimization problems that involve partial coupling among both local constraints and local cost functions. While the collection of networked agents is interested in discov ...

This work studies the problem of inferring from streaming data whether an agent is directly influenced by another agent over an adaptive network of interacting agents. Agent i influences agent j if they are connected, and if agent j uses the information fr ...

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 ...

This work considers multi-agent sharing optimization problems, where each agent owns a local smooth function plus a non-smooth function, and the network seeks to minimize the sum of all local functions plus a coupling composite function (possibly non-smoot ...

Most works on multi-agent reinforcement learning focus on scenarios where the state of the environment is fully observable. In this work, we consider a cooperative policy evaluation task in which agents are not assumed to observe the environment state dire ...

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 ...

The adaptive social learning paradigm helps model how networked agents are able to form opinions on a state of nature and track its drifts in a changing environment. In this framework, the agents repeatedly update their beliefs based on private observation ...

In diffusion social learning over weakly-connected graphs, it has been shown recently that influential agents shape the beliefs of non-influential agents. This paper analyzes this mechanism more closely and addresses two main questions. First, the article ...

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 ...

Ali H. Sayed

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Graph Learning Under Partial Observability

Learning From Heterogeneous Data Based on Social Interactions Over Graphs

Estimating the Topology of Preferential Attachment Graphs Under Partial Observability

Interplay Between Topology and Social Learning Over Weak Graphs

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

Quantization for Decentralized Learning Under Subspace Constraints

Frequency-Domain Diffusion Adaptation Over Networks

Adaptive Social Learning

Network Classifiers Based On Social Learning

Learning Over Multitask Graphs-Part II: Performance Analysis

Self-Aware Social Learning Over Graphs

Distributed Coupled Learning Over Adaptive Networks

Tomography Of Adaptive Multi-Agent Networks Under Limited Observation

Distributed Learning over Networks under Subspace Constraints

Dual Consensus Proximal Algorithm for Multi-Agent Sharing Problems

Policy Evaluation in Decentralized POMDPs With Belief Sharing

Dynamic Federated Learning

Discovering Influencers in Opinion Formation Over Social Graphs

Belief Control Strategies for Interactions Over Weakly-Connected Graphs

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