5 citations · 16 across the 14 of their papers we have counts for
15 papers
Improved Analysis of the Accelerated Noisy Power Method with Applications to Decentralized PCA
Pierre Aguié, Mathieu Even, Laurent Massoulié
We analyze the Accelerated Noisy Power Method, an algorithm for Principal Component Analysis in the setting where only inexact matrix-vector products are available, which can arise…
Meta-learning of shared linear representations beyond well-specified linear regression
Mathieu Even, Laurent Massoulié
Motivated by multi-task and meta-learning approaches, we consider the problem of learning structure shared by tasks or users, such as shared low-rank representations or clustered s…
Aligning Embeddings and Geometric Random Graphs: Informational Results and Computational Approaches for the Procrustes-Wasserstein Problem
Mathieu Even, Luca Ganassali, Jakob Maier +1
The Procrustes-Wasserstein problem consists in matching two high-dimensional point clouds in an unsupervised setting, and has many applications in natural language processing and c…
Noiseless Privacy-Preserving Decentralized Learning
Sayan Biswas, Mathieu Even, Anne-Marie Kermarrec +4
Decentralized learning (DL) enables collaborative learning without a server and without training data leaving the users' devices. However, the models shared in DL can still be used…
Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization
Mathieu Even, Anastasia Koloskova, Laurent Massoulié
Decentralized and asynchronous communications are two popular techniques to speedup communication complexity of distributed machine learning, by respectively removing the dependenc…
Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oracles
Kevin Scaman, Mathieu Even, Batiste Le Bars +1
In this paper, our aim is to analyse the generalization capabilities of first-order methods for statistical learning in multiple, different yet related, scenarios including supervi…
Stochastic Gradient Descent under Markovian Sampling Schemes
Mathieu Even
We study a variation of vanilla stochastic gradient descent where the optimizer only has access to a Markovian sampling scheme. These schemes encompass applications that range from…
(S)GD over Diagonal Linear Networks: Implicit Regularisation, Large Stepsizes and Edge of Stability
Mathieu Even, Scott Pesme, Suriya Gunasekar +1
In this paper, we investigate the impact of stochasticity and large stepsizes on the implicit regularisation of gradient descent (GD) and stochastic gradient descent (SGD) over dia…
Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays
Konstantin Mishchenko, Francis Bach, Mathieu Even +1
The existing analysis of asynchronous stochastic gradient descent (SGD) degrades dramatically when any delay is large, giving the impression that performance depends primarily on t…
Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging
Edwige Cyffers, Mathieu Even, Aurélien Bellet +1
Decentralized optimization is increasingly popular in machine learning for its scalability and efficiency. Intuitively, it should also provide better privacy guarantees, as nodes o…
Sample Optimality and All-for-all Strategies in Personalized Federated and Collaborative Learning
Mathieu Even, Laurent Massoulié, Kevin Scaman
In personalized Federated Learning, each member of a potentially large set of agents aims to train a model minimizing its loss function averaged over its local data distribution. W…
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…
Concentration of Non-Isotropic Random Tensors with Applications to Learning and Empirical Risk Minimization
Mathieu Even, Laurent Massoulié
Dimension is an inherent bottleneck to some modern learning tasks, where optimization methods suffer from the size of the data. In this paper, we study non-isotropic distributions…
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…