471 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Institut national de recherche en sciences et technologies du numériqueFR21 papers
- École Normale Supérieure - PSLFR17 papers
- Centre National de la Recherche ScientifiqueFR8 papers
- University of FloridaUS7 papers
- Département d'InformatiqueFR6 papers
- KTH Royal Institute of TechnologySE4 papers
- Télécom ParisFR4 papers
- The University of Texas at AustinUS4 papers
- Eindhoven University of TechnologyNL3 papers
- École Normale SupérieureBI2 papers
- Laboratoire des signaux et systèmesFR2 papers
- Microsoft (United States)US2 papers
52 papers
On Non-Linear operators for Geometric Deep Learning
Grégoire Sergeant-Perthuis, Jakob Maier, Joan Bruna +1
This work studies operators mapping vector and scalar fields defined over a manifold , and which commute with its group of diffeomorphisms .…
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…
Analysis of a Spatially Correlated Vehicular Network Assisted by Cox-distributed Vehicle Relays
Chang-Sik Choi, François Baccelli
In vehicle-to-all (V2X) communications, roadside units (RSUs) play an essential role in connecting various network devices. In some cases, users may not be well-served by RSUs due…
Coupling from the Past for the Null Recurrent Markov Chain
François Baccelli, Mir-Omid Haji-Mirsadeghi, Sayeh Khaniha
The Doeblin Graph of a countable state space Markov chain describes the joint pathwise evolutions of the Markov dynamics starting from all possible initial conditions, with two pat…
Online Stochastic Matching: A Polytope Perspective
C{é}line Comte, Fabien Mathieu, Sushil Mahavir Varma +1
Stochastic dynamic matching problems have recently gained attention in the stochastic-modeling community due to their diverse applications, such as supply-chain management and kidn…
Particle gradient descent model for point process generation
Antoine Brochard, Bartłomiej Błaszczyszyn, Stéphane Mallat +1
This paper presents a statistical model for stationary ergodic point processes, estimated from a single realization observed in a square window. With existing approaches in stochas…
Expressive Power of Invariant and Equivariant Graph Neural Networks
Waïss Azizian, Marc Lelarge
Various classes of Graph Neural Networks (GNN) have been proposed and shown to be successful in a wide range of applications with graph structured data. In this paper, we propose a…
Understanding and monitoring the evolution of the Covid-19 epidemic from medical emergency calls: the example of the Paris area
Stéphane Gaubert, Marianne Akian, Xavier Allamigeon +16
We portray the evolution of the Covid-19 epidemic during the crisis of March-April 2020 in the Paris area, by analyzing the medical emergency calls received by the EMS of the four…
Replica-Mean-Field Limits of Fragmentation-Interaction-Aggregation Processes
François Baccelli, Michel Davydov, Thibaud Taillefumier
Network dynamics with point-process-based interactions are of paramount modeling interest. Unfortunately, most relevant dynamics involve complex graphs of interactions for which an…
From tree matching to sparse graph alignment
Luca Ganassali, Laurent Massoulié
In this paper we consider alignment of sparse graphs, for which we introduce the Neighborhood Tree Matching Algorithm (NTMA). For correlated Erdős-Rényi random graphs, we prove tha…
Spectral Alignment of Correlated Gaussian matrices
Luca Ganassali, Marc Lelarge, Laurent Massoulié
In this paper we analyze a simple spectral method (EIG1) for the problem of matrix alignment, consisting in aligning their leading eigenvectors: given two matrices and , we…
Zap Q-Learning With Nonlinear Function Approximation
Shuhang Chen, Adithya M. Devraj, Fan Lu +2
Zap Q-learning is a recent class of reinforcement learning algorithms, motivated primarily as a means to accelerate convergence. Stability theory has been absent outside of two res…
Eigenvalues of the non-backtracking operator detached from the bulk
Simon Coste, Yizhe Zhu
We describe the non-backtracking spectrum of a stochastic block model with connection probabilities . In this regime we answer a qu…
Zap Q-Learning for Optimal Stopping Time Problems
Shuhang Chen, Adithya M. Devraj, Ana Bušić +1
The objective in this paper is to obtain fast converging reinforcement learning algorithms to approximate solutions to the problem of discounted cost optimal stopping in an irreduc…
Continuum Line-of-Sight Percolation on Poisson-Voronoi Tessellations
Quentin Le Gall, Bartłomiej Błaszczyszyn, Elie Cali +1
In this work, we study a new model for continuum line-of-sight percolation in a random environment driven by the Poisson-Voronoi tessellation in the -dimensional Euclidean space…
A Unified Framework for Generalizing the Gromov-Hausdorff Metric
Ali Khezeli
In this paper, an approach for generalizing the Gromov-Hausdorff metric is presented, which applies to metric spaces equipped with some additional structure. A special case is the…
Statistical thresholds for Tensor PCA
Aukosh Jagannath, Patrick Lopatto, Leo Miolane
We study the statistical limits of testing and estimation for a rank one deformation of a Gaussian random tensor. We compute the sharp thresholds for hypothesis testing and estimat…
Analyzing LoRa long-range, low-power, wide-area networks using stochastic geometry
Bartłomiej Błaszczyszyn, Paul Mühlethaler
In this paper we present a simple, stochastic-geometric model of a wireless access network exploiting the LoRA (Long Range) protocol, which is a non-expensive technology allowing f…
Robustness of spectral methods for community detection
Ludovic Stephan, Laurent Massoulié
The present work is concerned with community detection. Specifically, we consider a random graph drawn according to the stochastic block model~: its vertex set is partitioned into…
Planting trees in graphs, and finding them back
Laurent Massoulié, Ludovic Stephan, Don Towsley
In this paper we study detection and reconstruction of planted structures in Erdős-Rényi random graphs. Motivated by a problem of communication security, we focus on planted struct…
Weighted Spectral Embedding of Graphs
Thomas Bonald, Alexandre Hollocou, Marc Lelarge
We present a novel spectral embedding of graphs that incorporates weights assigned to the nodes, quantifying their relative importance. This spectral embedding is based on the firs…
Action-Constrained Markov Decision Processes With Kullback-Leibler Cost
Ana Bušić, Sean Meyn
This paper concerns computation of optimal policies in which the one-step reward function contains a cost term that models Kullback-Leibler divergence with respect to nominal dynam…
Hierarchical Graph Clustering using Node Pair Sampling
Thomas Bonald, Bertrand Charpentier, Alexis Galland +1
We present a novel hierarchical graph clustering algorithm inspired by modularity-based clustering techniques. The algorithm is agglomerative and based on a simple distance between…
Efficient inference in stochastic block models with vertex labels
Clara Stegehuis, Laurent Massoulié
We study the stochastic block model with two communities where vertices contain side information in the form of a vertex label. These vertex labels may have arbitrary label distrib…