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20242026
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stat.ML2026

Phase Transition in Convex Relaxations for Graph Alignment

Laurent Massoulié, Sushil Mahavir Varma, Louis Vassaux +1

We study the graph alignment problem for correlated Gaussian Orthogonal Ensemble (GOE) matrices, where the goal is to recover a hidden vertex permutation given two correlated symme…

stat.ML2026

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…

stat.ML2025

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…

stat.ML2025

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…

stat.ML2024

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…