168 citations · 178 across the 4 of their papers we have counts for
7 papers
Impossibility of Partial Recovery in the Graph Alignment Problem
Luca Ganassali, Laurent Massoulié, Marc Lelarge
Random graph alignment refers to recovering the underlying vertex correspondence between two random graphs with correlated edges. This can be viewed as an average-case and noisy ve…
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…
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…
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…
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…
Accelerated Decentralized Optimization with Local Updates for Smooth and Strongly Convex Objectives
Hadrien Hendrikx, Francis Bach, Laurent Massoulié
In this paper, we study the problem of minimizing a sum of smooth and strongly convex functions split over the nodes of a network in a decentralized fashion. We propose the algorit…