activity
20062021
most citedOptimal algorithms for smooth and strongly convex distributed optimization in networks

190 citations · 657 across the 32 of their papers we have counts for

collaborators

39 papers

cs.DS2021★ 8 cited

Correlation detection in trees for planted graph alignment

Luca Ganassali, Laurent Massoulié, Marc Lelarge

Motivated by alignment of correlated sparse random graphs, we introduce a hypothesis testing problem of deciding whether or not two random trees are correlated. We obtain sufficien…

math.OC2021

Asynchronous speedup in decentralized optimization

Mathieu Even, Hadrien Hendrikx, Laurent Massoulie

In decentralized optimization, nodes of a communication network each possess a local objective function, and communicate using gossip-based methods in order to minimize the average…

math.OC2020

An Optimal Algorithm for Decentralized Finite Sum Optimization

Hadrien Hendrikx, Francis Bach, Laurent Massoulie

Modern large-scale finite-sum optimization relies on two key aspects: distribution and stochastic updates. For smooth and strongly convex problems, existing decentralized algorithm…

stat.AP2020★ 18 cited

Understanding and monitoring the evolution of the Covid-19 epidemic from medical emergency calls: the example of the Paris area

Stéphane Gaubert, Marianne Akian, Xavier Allamigeon +16

We portray the evolution of the Covid-19 epidemic during the crisis of March-April 2020 in the Paris area, by analyzing the medical emergency calls received by the EMS of the four…

math.OC2020★ 16 cited

Statistically Preconditioned Accelerated Gradient Method for Distributed Optimization

Hadrien Hendrikx, Lin Xiao, Sebastien Bubeck +2

We consider the setting of distributed empirical risk minimization where multiple machines compute the gradients in parallel and a centralized server updates the model parameters.…

math.OC2019★ 16 cited

An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums

Hadrien Hendrikx, Francis Bach, Laurent Massoulie

Modern large-scale finite-sum optimization relies on two key aspects: distribution and stochastic updates. For smooth and strongly convex problems, existing decentralized algorithm…