Publications by Ali H. Sayed | EPFL Graph Search

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In this paper we develop a fully decentralized algorithm for policy evaluation with off-policy learning and linear function approximation. The proposed algorithm is of the variance reduced kind and achieves linear convergence with O(1) memory requirements. ...

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 enforce the objectives across the network to lie in a low-dimensiona ...

Under uncertain channel conditions, local and global power control factors for amplify-and-forward relay processing and source-destination beamforming are jointly and iteratively designed based on a minimum mean-square-error (MMSE) criterion. The influence ...

Graph filters, defined as polynomial functions of a graph-shift operator (GSO), play a key role in signal processing over graphs. In this work, we are interested in the adaptive and distributed estimation of graph filter coefficients from streaming graph s ...

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 ...

In empirical risk optimization, it has been observed that stochastic gradient implementations that rely on random reshuffling of the data achieve better performance than implementations that rely on sampling the data uniformly. Recent works have pursued ju ...

This work studies the learning process over social networks under partial and random information sharing. In traditional social learning models, agents exchange full belief information with each other while trying to infer the true state of nature. We stud ...

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. ...

Distributed decision-making over networks involves multiple agents collaborating to achieve a common goal. In the social learning process, where agents aim at inferring an unknown state from a stream of local observations, the probability of error in their ...

A common assumption in the social learning literature is that agents exchange information in an unselfish manner. In this work, we consider the scenario where a subset of agents aims at driving the network beliefs to the wrong hypothesis. The adversaries a ...

This article studies a class of nonsmooth decentralized multiagent optimization problems where the agents aim at minimizing a sum of local strongly-convex smooth components plus a common nonsmooth term. We propose a general primal-dual algorithmic framewor ...

In this work, we examine a network of agents operating asynchronously, aiming to discover an ideal global model that suits individual local datasets. Our assumption is that each agent independently chooses when to participate throughout the algorithm and t ...

In network semi-supervised learning problems, only a subset of the network nodes is able to access the data labeling. This paper formulates a decentralized optimization problem where agents have individual decision rules to estimate, subject to the conditi ...

In this paper, we design a multiuser two-way relay system using space division multiple access (SDMA) communications and devise an optimal scheduling method that maximizes the sum rate while ensuring fairness among users. To reduce the computational load a ...

Various bias-correction methods such as EXTRA, gradient tracking methods, and exact diffusion have been proposed recently to solve distributed deterministic optimization problems. These methods employ constant step-sizes and converge linearly to the exact ...

Adaptive Social Learning (ASL) enables consistent truth learning in nonstationary environments. In this framework, agents linked by a graph, exchange their local beliefs with neighbors to track some underlying state of interest. This state can drift over t ...

This work examines a social learning problem, where dispersed agents connected through a network topology interact locally to form their opinions (beliefs) as regards certain hypotheses of interest. These opinions evolve over time, since the agents collect ...

We consider an adaptive network made of interconnected agents engaged in a binary decision task. It is assumed that the agents cannot deliver full-precision messages to their neighbors, but only binary messages. For this scenario, a modified version of the ...

The DEF-ATC (Differential Error Feedback - Adapt Then Combine) approach is a novel strategy to address decentralized learning and optimization problems under communication constraints. The strategy blends differential quantization and error feedback to mit ...

This work derives and analyzes an online learning strategy for tracking the average of time-varying distributed signals by relying on randomized coordinate-descent updates. During each iteration, each agent selects or observes a random entry of the observa ...

Ali H. Sayed

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Distributed Value-Function Learning with Linear Convergence Rates

Distributed Inference Over Networks Under Subspace Constraints

Power Allocation for Beamforming Relay Networks under Channel Uncertainties

A Preconditioned Graph Diffusion LMS for Adaptive Graph Signal Processing

Distributed Coupled Multiagent Stochastic Optimization

Stochastic Learning Under Random Reshuffling With Constant Step-Sizes

Social Opinion Formation and Decision Making Under Communication Trends

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

Optimal Combination Policies for Error Exponent Maximization in Social Learning

Social Learning Under Inferential Attacks

Decentralized Proximal Gradient Algorithms With Linear Convergence Rates

Asynchronous Diffusion Learning with Agent Subsampling and Local Updates

Decentralized Semi-supervised Learning over Multitask Graphs

User Selection Methods for Multiuser Two-Way Relay Communications Using Space Division Multiple Access

On the Influence of Bias-Correction on Distributed Stochastic Optimization

Adaptive Social Learning for Tracking Rare Transition Markov Chains

Memory-Aware Social Learning Under Partial Information Sharing

Decisions under Binary Messaging over Adaptive Networks

Matching centralized learning performance via compressed decentralized learning with error feedback

Dynamic Average Diffusion With Randomized Coordinate Updates