21 citations · 136 across the 22 of their papers we have counts for
11 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…
Investigating Variance Definitions for Mirror Descent with Relative Smoothness
Hadrien Hendrikx
Mirror Descent is a popular algorithm, that extends Gradients Descent (GD) beyond the Euclidean geometry. One of its benefits is to enable strong convergence guarantees through smo…
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…
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…
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.…
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…
Asynchronous Accelerated Proximal Stochastic Gradient for Strongly Convex Distributed Finite Sums
Hadrien Hendrikx, Francis Bach, Laurent Massoulié
In this work, we study the problem of minimizing the sum of strongly convex functions split over a network of nodes. We propose the decentralized and asynchronous algorithm ADF…
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…