Publications associées à Laboratoire de systèmes adaptatifs

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iPool--Information-Based Pooling in Hierarchical Graph Neural Networks

Pascal Frossard, Chenglin Li, Xing Gao

With the advent of data science, the analysis of network or graph data has become a very timely research problem. A variety of recent works have been proposed to generalize neural networks to graphs, either from a spectral graph theory or a spatial perspec ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Scalable Robust Graph Embedding with Spark

Karl Aberer, Quoc Viet Hung Nguyen, Chi Thang Duong, Trung-Dung Hoang

Graph embedding aims at learning a vector-based representation of vertices that incorporates the structure of the graph. This representation then enables inference of graph properties. Existing graph embedding techniques, however, do not scale well to larg ...

ASSOC COMPUTING MACHINERY2021

Decentralized Proximal Gradient Algorithms With Linear Convergence Rates

Ali H. Sayed, Kun Yuan, Sulaiman A S A E Alghunaim

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 ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Frequency-Domain Diffusion Adaptation Over Networks

Ali H. Sayed, Ricardo Merched, Shuo Zhang

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 ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Multilayer Graph Clustering With Optimized Node Embedding

Pascal Frossard, Mireille El Gheche

We are interested in multilayer graph clustering, which aims at dividing the graph nodes into categories or communities. To do so, we propose to learn a clustering-friendly embedding of the graph nodes by solving an optimization problem that involves a fid ...

IEEE2021

Distributed Learning in Non-Convex Environments-Part II: Polynomial Escape From Saddle-Points

Ali H. Sayed, Stefan Vlaski

The diffusion strategy for distributed learning from streaming data employs local stochastic gradient updates along with exchange of iterates over neighborhoods. In Part I [3] of this work we established that agents cluster around a network centroid and pr ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Distributed Meta-Learning with Networked Agents

Ali H. Sayed, Mert Kayaalp, Stefan Vlaski

Meta-learning aims to improve efficiency of learning new tasks by exploiting the inductive biases obtained from related tasks. Previous works consider centralized or federated architectures that rely on central processors, whereas, in this paper, we propos ...

EUROPEAN ASSOC SIGNAL SPEECH & IMAGE PROCESSING-EURASIP2021

Differentially Private Stochastic Coordinate Descent

Rachid Guerraoui, Georgios Damaskinos

In this paper we tackle the challenge of making the stochastic coordinate descent algorithm differentially private. Compared to the classical gradient descent algorithm where updates operate on a single model vector and controlled noise addition to this ve ...

AIAA2021

TDOA Source-Localization Technique Robust to Time-Synchronization Attacks

Jean-Yves Le Boudec, Marguerite Marie Nathalie Delcourt

In this paper, we focus on the localization of a passive source from time difference of arrival (TDOA) measurements. TDOA values are computed with respect to pairs of fixed sensors that are required to be accurately time-synchronized. This constitutes a we ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Laboratoire de systèmes adaptatifs

Graph Chatbot

Chattez avec Graph Search

iPool--Information-Based Pooling in Hierarchical Graph Neural Networks

Scalable Robust Graph Embedding with Spark

Decentralized Proximal Gradient Algorithms With Linear Convergence Rates

Frequency-Domain Diffusion Adaptation Over Networks

Multilayer Graph Clustering With Optimized Node Embedding

Distributed Learning in Non-Convex Environments-Part II: Polynomial Escape From Saddle-Points

Distributed Meta-Learning with Networked Agents

Differentially Private Stochastic Coordinate Descent

TDOA Source-Localization Technique Robust to Time-Synchronization Attacks

Competing Adaptive Networks

Decentralized Proximal Gradient Algorithms With Linear Convergence Rates

iPool--Information-Based Pooling in Hierarchical Graph Neural Networks

Distributed Meta-Learning with Networked Agents

Scalable Robust Graph Embedding with Spark

Competing Adaptive Networks

Differentially Private Stochastic Coordinate Descent

Multilayer Graph Clustering With Optimized Node Embedding

Frequency-Domain Diffusion Adaptation Over Networks

TDOA Source-Localization Technique Robust to Time-Synchronization Attacks

Distributed Learning in Non-Convex Environments-Part II: Polynomial Escape From Saddle-Points