21 citations · 100 across the 11 of their papers we have counts for
9 papers · 1 filter
Unified Breakdown Analysis for Byzantine Robust Gossip
Renaud Gaucher, Aymeric Dieuleveut, Hadrien Hendrikx
In decentralized machine learning, different devices communicate in a peer-to-peer manner to collaboratively learn from each other's data. Such approaches are vulnerable to misbeha…
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…
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.…