activity
20072021
most citedStructural properties of proportional fairness: stability and insensitivity

168 citations · 234 across the 10 of their papers we have counts for

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Showing 2020Show all

6 papers · 1 filter

cs.DC2020★ 2 cited

Asynchrony and Acceleration in Gossip Algorithms

Mathieu Even, Hadrien Hendrikx, Laurent Massoulié

This paper considers the minimization of a sum of smooth and strongly convex functions dispatched over the nodes of a communication network. Previous works on the subject either fo…

stat.AP2020★ 2 cited

Probabilistic and mean-field model of COVID-19 epidemics with user mobility and contact tracing

M. Akian, L. Ganassali, S. Gaubert +1

We propose a detailed discrete-time model of COVID-19 epidemics coming in two flavours, mean-field and probabilistic. The main contribution lies in several extensions of the basic…

stat.ML2020★ 4 cited

Partial Recovery in the Graph Alignment Problem

Georgina Hall, Laurent Massoulié

In this paper, we consider the graph alignment problem, which is the problem of recovering, given two graphs, a one-to-one mapping between nodes that maximizes edge overlap. This p…

math.OC2020★ 6 cited

Dual-Free Stochastic Decentralized Optimization with Variance Reduction

Hadrien Hendrikx, Francis Bach, Laurent Massoulié

We consider the problem of training machine learning models on distributed data in a decentralized way. For finite-sum problems, fast single-machine algorithms for large datasets r…

math.PR2020★ 3 cited

Non-backtracking spectra of weighted inhomogeneous random graphs

Ludovic Stephan, Laurent Massoulié

We study a model of random graphs where each edge is drawn independently (but not necessarily identically distributed) from the others, and then assigned a random weight. When the…

cs.DS2020★ 19 cited

From tree matching to sparse graph alignment

Luca Ganassali, Laurent Massoulié

In this paper we consider alignment of sparse graphs, for which we introduce the Neighborhood Tree Matching Algorithm (NTMA). For correlated Erdős-Rényi random graphs, we prove tha…