21 citations · 81 across the 8 of their papers we have counts for
11 papers
A principled framework for the design and analysis of token algorithms
Hadrien Hendrikx
We consider a decentralized optimization problem, in which nodes collaborate to optimize a global objective function using local communications only. While many decentralized a…
Fast Stochastic Bregman Gradient Methods: Sharp Analysis and Variance Reduction
Radu-Alexandru Dragomir, Mathieu Even, Hadrien Hendrikx
We study the problem of minimizing a relatively-smooth convex function using stochastic Bregman gradient methods. We first prove the convergence of Bregman Stochastic Gradient Desc…
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…
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…
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…
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.…