Publications de Ali H. Sayed | EPFL Graph Search

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This article reviews significant advances in networked signal and information processing (SIP), which have enabled in the last 25 years extending decision making and inference, optimization, control, and learning to the increasingly ubiquitous environments ...

This work introduces and studies the convergence of a stochastic diffusion-optimistic learning (DOL) strategy for solving distributed nonconvex (NC) and Polyak-Lojasiewicz (PL) min-max optimization problems. Problems of this type are of interest due to a w ...

Social learning strategies implement a distributed decision-making process where several agents, connected over a graph, exchange their beliefs about some hypotheses of interest. Each agent uses some local model that describes the marginal distribution of ...

The utilization of online stochastic algorithms is popular in large-scale learning settings due to their ability to compute updates on the fly, without the need to store and process data in large batches. When a constant step-size is used, these algorithms ...

We examine the learning performance achievable by a network of agents that solve a distributed regression problem using the recently proposed ACTC (Adapt-Compress-Then-Combine) diffusion strategy. The agents operate under communication constraints: they ar ...

Social learning algorithms provide a model for the formation and propagation of opinions over social networks. However, most studies focus on the case in which agents share their information synchronously over regular intervals. In this work, we analyze be ...

In social learning, a network of agents assigns probability scores (beliefs) to some hypotheses of interest, based on the observation of streaming data. First, each agent updates locally its belief with the information extracted from the current data throu ...

Traditional social learning frameworks consider environments with a homogeneous state, where each agent receives observations conditioned on that true state of nature. In this work, we relax this assumption and study the distributed hypothesis testing prob ...

This work considers a diffusion network responding to streaming data, and studies the problem of identifying the topology of a subnetwork of observable agents by tracking their output measurements. Topology inference from indirect and/or incomplete dataset ...

This work proposes a multi-agent filtering algorithm over graphs for finite-state hidden Markov models (HMMs), which can be used for sequential state estimation or for tracking opinion formation over dynamic social networks. We show that the difference fro ...

This paper develops a distributed variance-reduced strategy for a collection of interacting agents that are connected by a graph topology. The resulting diffusion-AVRG (where AVRG stands for "amortized variance-reduced gradient") algorithm is shown to have ...

We propose a distributed least-mean squares (LMS) procedure based on a diffusion strategy for localization and tracking of mobile terminals in cellular networks. In the proposed algorithm, collaborating base stations measure two sets of parameters, namely, ...

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 ...

This work employs a social learning strategy to estimate the global state in a partially observable multi-agent reinforcement learning (MARL) setting. We prove that the proposed methodology can achieve results within an ε-neighborhood of the solution for a ...

In this paper, we study a dynamic game between two networks. The networks compete by optimizing two coupled objective functions. Agents within the same network work toward a common goal and are regarded as cooperative agents; they exchange their strategies ...

Ali H. Sayed

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Networked Signal and Information Processing: Learning by multiagent systems

Diffusion Optimistic Learning for Min-Max Optimization

Social Learning with Dependent Agents

Tracking Performance of Online Stochastic Learners

Compressed Distributed Regression over Adaptive Networks

Asynchronous Social Learning

Social Learning with Adaptive Models

Social Learning in Community Structured Graphs

Consistent Tomography over Diffusion Networks under the Low-Observability Regime

Hidden Markov Modeling Over Graphs

Variance-Reduced Stochastic Learning by Networked Agents Under Random Reshuffling

Diffusion LMS localization and tracking algorithm for wireless cellular networks

On the Trade-off between Flatness and Optimization in Distributed Learning

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

Multi-Agent Reinforcement Learning in Partially Observable Environments Using Social Learning

Diffusion Learning Over Adaptive Competing Networks

Online Graph Learning from Sequential Data

Decentralized Adversarial Training over Graphs