Publications de Ali H. Sayed | EPFL Graph Search

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This letter proposes a general regularization framework for inference over multitask networks. The optimization approach relies on minimizing a global cost consisting of the aggregate sum of individual costs regularized by a term that allows to incorporate ...

We consider a network of agents that must solve an online optimization problem from continual observation of streaming data. To this end, the agents implement a distributed cooperative strategy where each agent is allowed to perform local exchange of infor ...

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, ...

We study the asymptotic learning rates of belief vectors in a distributed hypothesis testing problem under linear and log-linear combination rules. We show that under both combination strategies, agents are able to learn the truth exponentially fast, with ...

Part I of this paper developed the exact diffusion algorithm to remove the bias that is characteristic of distributed solutions for deterministic optimization problems. The algorithm was shown to be applicable to the larger set of locally balanced left-sto ...

In this work we consider the problem of learning an Erdos-Renyi graph over a diffusion network when: i) data from only a limited subset of nodes are available (partial observation); ii) and the inferential goal is to discover the graph of interconnections ...

This paper formulates 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. The smoothness requirement softens the tra ...

Driven by the need to solve increasingly complex optimization problems in signal processing and machine learning, there has been increasing interest in understanding the behavior of gradient-descent algorithms in non-convex environments. Most available wor ...

In this paper, we consider a setting where heterogeneous agents with connectivity are performing inference using unlabeled streaming data. Observed data are only partially informative about the target variable of interest. In order to overcome the uncertai ...

This paper develops a distributed optimization strategy with guaranteed exact convergence for a broad class of left-stochastic combination policies. The resulting exact diffusion strategy is shown in Part II of this paper to have a wider stability range an ...

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 ...

This article considers a network of agents interested in solving a classification t ask. The datasets available to accomplish the task are heterogeneous and dispersed across the agents. Each agent is inter-ested in discriminating among the 'inner' hypothes ...

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 ...

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

This work examines the problem of learning a network graph from signals emitted by the network nodes, according to a diffusion model ruled by a Laplacian combination policy. The challenging regime of partial observability is considered, where signals are c ...

This paper presents an adaptive combination strategy for distributed learning over diffusion networks. Since learning relies on the collaborative processing of the stochastic information at the dispersed agents, the overall performance can be improved by d ...

We consider a collaborative decision-making framework where heterogeneous agents receive streaming and partially informative observations. We consider two asynchronous scenarios that differ based on the agents' participation patterns and the fusion center' ...

This paper studies the probability of error associated with the social machine learning framework, which involves an independent training phase followed by a cooperative decision-making phase over a graph. This framework addresses the problem of classifyin ...

By 'social learning,' in this article we refer to mechanisms for opinion formation and decision making over graphs and the study of how agents' decisions evolve dynamically through interactions with neighbors and the environment. The study of social learni ...

We consider a distributed social learning problem where a network of agents is interested in selecting one among a finite number of hypotheses. The data collected by the agents might be heterogeneous, meaning that different sub-networks might observe data ...

Ali H. Sayed

Chattez avec Graph Search

A Regularization Framework for Learning Over Multitask Graphs

Distributed Adaptive Learning Under Communication Constraints

A Proximal Diffusion Strategy for Multiagent Optimization With Sparse Affine Constraints

On the Arithmetic and Geometric Fusion of Beliefs for Distributed Inference

Exact Diffusion for Distributed Optimization and Learning-Part II: Convergence Analysis

Graph Learning with Partial Observations: Role of Degree Concentration

Distributed Inference over Multitask Graphs under Smoothness

Distributed Learning in Non-Convex Environments-Part I: Agreement at a Linear Rate

Causal Influence in Federated Edge Inference

Exact Diffusion for Distributed Optimization and Learning-Part I: Algorithm Development

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

DECENTRALIZED FUSION OF EXPERTS OVER NETWORKS

Diffusion LMS With Communication Delays: Stability and Performance Analysis

Social Learning with Disparate Hypotheses

Learning Dynamic Graphs under Partial Observability

Gramian-Based Adaptive Combination Policies For Diffusion Learning Over Networks

Causal Impact Analysis for Asynchronous Decision Making

Non-asymptotic performance of social machine learning under limited data

Socially Intelligent Networks: A framework for decision making over graphs

Exponential Collapse Of Social Beliefs Over Weakly-Connected Heterogeneous Networks