4 citations · 5 across the 6 of their papers we have counts for
7 papers
A Multiresolution Analysis Framework for the Statistical Analysis of Incomplete Rankings
Eric Sibony, Stéphan Clémençon, Jérémie Jakubowicz
Though the statistical analysis of ranking data has been a subject of interest over the past centuries, especially in economics, psychology or social choice theory, it has been rev…
Random Pairwise Gossip on Metric Spaces
Anass Bellachehab, Jérémie Jakubowicz
In the context of sensor networks, gossip algorithms are a popular, well esthablished technique for achieving consensus when sensor data is encoded in linear spaces. Gossip algorit…
Multiresolution Analysis of Incomplete Rankings
Stéphan Clémençon, Jérémie Jakubowicz, Eric Sibony
Incomplete rankings on a set of items are orderings of the form , with and . T…
Robust Consensus in Distributed Networks using Total Variation
Walid Ben-Ameur, Pascal Bianchi, Jérémie Jakubowicz
Consider a connected network of agents endowed with local cost functions representing private objectives. Agents seek to find an agreement on some minimizer of the aggregate cost,…
Convergence of a Multi-Agent Projected Stochastic Gradient Algorithm for Non-Convex Optimization
Pascal Bianchi, Jérémie Jakubowicz
We introduce a new framework for the convergence analysis of a class of distributed constrained non-convex optimization algorithms in multi-agent systems. The aim is to search for…
Distributed Stochastic Approximation for Constrained and Unconstrained Optimization
Pascal Bianchi, Jérémie Jakubowicz
In this paper, we analyze the convergence of a distributed Robbins-Monro algorithm for both constrained and unconstrained optimization in multi-agent systems. The algorithm searche…
Neyman-Pearson Detection of a Gaussian Source using Dumb Wireless Sensors
Pascal Bianchi, Jeremie Jakubowicz, Francois Roueff
We investigate the performance of the Neyman-Pearson detection of a stationary Gaussian process in noise, using a large wireless sensor network (WSN). In our model, each sensor com…