2 citations · 2 across the 14 of their papers we have counts for
8 papers · 1 filter
Performance of Gaussian Boson Sampling on Planted Bipartite Clique Detection
Yu-Zhen Janice Chen, Laurent Massoulié, Don Towsley
We investigate whether Gaussian Boson Sampling (GBS) can provide a computational advantage for solving the planted biclique problem, which is a graph problem widely believed to be…
Computational thresholds in high-dimensional statistics: the case of graph alignment
Laurent Massoulié
In this article we consider the graph alignment problem from the perspective of high-dimensional statistics: we aim to estimate an unknown permutation from the observation of…
Asynchronous Collective Tree Exploration: a Distributed Algorithm, and a new Lower Bound
Romain Cosson, Laurent Massoulié
We study the problem of collective tree exploration in which a team of mobile agents must collectively visit all nodes of an unknown tree in as few moves as possible. The agent…
Adaptive collaboration for online personalized distributed learning with heterogeneous clients
Constantin Philippenko, Batiste Le Bars, Kevin Scaman +1
We study the problem of online personalized decentralized learning with statistically heterogeneous clients collaborating to accelerate local training. An important challenge i…
Asymmetric graph alignment and the phase transition for asymmetric tree correlation testing
Jakob Maier, Laurent Massoulié
Graph alignment - identifying node correspondences between two graphs - is a fundamental problem with applications in network analysis, biology, and privacy research. While substan…
Graph Alignment via Birkhoff Relaxation
Sushil Mahavir Varma, Irène Waldspurger, Laurent Massoulié
We consider the graph alignment problem, wherein the objective is to find a vertex correspondence between two graphs that maximizes the edge overlap. The graph alignment problem is…
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…
The feasibility of multi-graph alignment: a Bayesian approach
Louis Vassaux, Laurent Massoulié
We establish thresholds for the feasibility of random multi-graph alignment in two models. In the Gaussian model, we demonstrate an "all-or-nothing" phenomenon: above a critical th…