190 citations · 442 across the 17 of their papers we have counts for
20 papers
In-depth Analysis of Low-rank Matrix Factorisation in a Federated Setting
Constantin Philippenko, Kevin Scaman, Laurent Massoulié
We analyze a distributed algorithm to compute a low-rank matrix factorization on clients, each holding a local dataset , mathematica…
Black-box Acceleration of Las Vegas Algorithms and Algorithmic Reverse Jensen's Inequalities
Kevin Scaman
Let be a Las Vegas algorithm, i.e. an algorithm whose running time is a random variable drawn according to a certain probability distribution . In 1993, Luby,…
Convergence beyond the over-parameterized regime using Rayleigh quotients
David A. R. Robin, Kevin Scaman, Marc Lelarge
In this paper, we present a new strategy to prove the convergence of deep learning architectures to a zero training (or even testing) loss by gradient flow. Our analysis is centere…
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…
Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize
Alain Durmus, Eric Moulines, Alexey Naumov +3
This paper provides a non-asymptotic analysis of linear stochastic approximation (LSA) algorithms with fixed stepsize. This family of methods arises in many machine learning tasks…
Lipschitz Normalization for Self-Attention Layers with Application to Graph Neural Networks
George Dasoulas, Kevin Scaman, Aladin Virmaux
Attention based neural networks are state of the art in a large range of applications. However, their performance tends to degrade when the number of layers increases. In this work…
Improving Hierarchical Adversarial Robustness of Deep Neural Networks
Avery Ma, Aladin Virmaux, Kevin Scaman +1
Do all adversarial examples have the same consequences? An autonomous driving system misclassifying a pedestrian as a car may induce a far more dangerous -- and even potentially le…
Ego-based Entropy Measures for Structural Representations on Graphs
George Dasoulas, Giannis Nikolentzos, Kevin Scaman +2
Machine learning on graph-structured data has attracted high research interest due to the emergence of Graph Neural Networks (GNNs). Most of the proposed GNNs are based on the node…
Ego-based Entropy Measures for Structural Representations
George Dasoulas, Giannis Nikolentzos, Kevin Scaman +2
In complex networks, nodes that share similar structural characteristics often exhibit similar roles (e.g type of users in a social network or the hierarchical position of employee…
Coloring graph neural networks for node disambiguation
George Dasoulas, Ludovic Dos Santos, Kevin Scaman +1
In this paper, we show that a simple coloring scheme can improve, both theoretically and empirically, the expressive power of Message Passing Neural Networks(MPNNs). More specifica…
Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning
Igor Colin, Ludovic Dos Santos, Kevin Scaman
We investigate the theoretical limits of pipeline parallel learning of deep learning architectures, a distributed setup in which the computation is distributed per layer instead of…
Optimal Algorithms for Non-Smooth Distributed Optimization in Networks
Kevin Scaman, Francis Bach, Sébastien Bubeck +2
In this work, we consider the distributed optimization of non-smooth convex functions using a network of computing units. We investigate this problem under two regularity assumptio…
KONG: Kernels for ordered-neighborhood graphs
Moez Draief, Konstantin Kutzkov, Kevin Scaman +1
We present novel graph kernels for graphs with node and edge labels that have ordered neighborhoods, i.e. when neighbor nodes follow an order. Graphs with ordered neighborhoods are…
Lipschitz regularity of deep neural networks: analysis and efficient estimation
Kevin Scaman, Aladin Virmaux
Deep neural networks are notorious for being sensitive to small well-chosen perturbations, and estimating the regularity of such architectures is of utmost importance for safe and…
A Spectral Method for Activity Shaping in Continuous-Time Information Cascades
Kevin Scaman, Argyris Kalogeratos, Luca Corinzia +1
Information Cascades Model captures dynamical properties of user activity in a social network. In this work, we develop a novel framework for activity shaping under the Continuous-…
Optimal algorithms for smooth and strongly convex distributed optimization in networks
Kevin Scaman, Francis Bach, Sébastien Bubeck +2
In this paper, we determine the optimal convergence rates for strongly convex and smooth distributed optimization in two settings: centralized and decentralized communications over…
Spectral Bounds in Random Graphs Applied to Spreading Phenomena and Percolation
Rémi Lemonnier, Kevin Scaman, Nicolas Vayatis
In this paper, we derive nonasymptotic theoretical bounds for the influence in random graphs that depend on the spectral radius of a particular matrix, called the Hazard matrix. We…
Multivariate Hawkes Processes for Large-scale Inference
Rémi Lemonnier, Kevin Scaman, Argyris Kalogeratos
In this paper, we present a framework for fitting multivariate Hawkes processes for large-scale problems both in the number of events in the observed history and the number of…
What Makes a Good Plan? An Efficient Planning Approach to Control Diffusion Processes in Networks
Kevin Scaman, Argyris Kalogeratos, Nicolas Vayatis
In this paper, we analyze the quality of a large class of simple dynamic resource allocation (DRA) strategies which we name priority planning. Their aim is to control an undesired…
Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology
Remi Lemonnier, Kevin Scaman, Nicolas Vayatis
In this paper, we derive theoretical bounds for the long-term influence of a node in an Independent Cascade Model (ICM). We relate these bounds to the spectral radius of a particul…