Hugo Jaquard

About

I am a PhD student at GIPSA-lab (UMR 5216 CNRS, Grenoble-INP, Université Grenoble-Alpes), working in the GAIA team, under the supervision of Pierre-Olivier Amblard, Simon Barthelmé and Nicolas Tremblay.

My work focuses on random decompositions of graphs, and how to use them in Randomized Linear Algebra. I am especially interested in Determinantal Point Processes defined over graphs endowed with additional geometric or topological structure (e.g., a connection and parallel transports maps, or cell complexes), a type of data that can appear in very diverse settings.

I hold both a master degree in computer science from Université de Bordeaux (with a focus on formal verification), and a master degree in mathematics from Université Grenoble-Alpes (focused on groups and geometry).

List of Publications

Preprints

Random Multi-Type Spanning Forests for Synchronization on Sparse Graphs (click to preview)

Hugo Jaquard, Pierre-Olivier Amblard, Simon Barthelmé, Nicolas Tremblay

Random diffusions are a popular tool in Monte-Carlo estimations, with well established algorithms such as Walk-on-Spheres (WoS) going back several decades. In this work, we introduce diffusion estimators for the problems of angular synchronization and smoothing on graphs, in the presence of a rotation associated to each edge. Unlike classical WoS algorithms, these estimators allow for global estimations by propagating along the branches of multi-type spanning forests, and we show that they can outperform standard numerical-linear-algebra solvers in challenging instances, depending on the topology and density of the graph. Submitted.
arxiv:2403.19300, code on gitlab

Conference Proceedings

Smoothing complex-valued signals on Graphs with Monte-Carlo (click to preview extended version)

Hugo Jaquard, Michaël Fanuel, Pierre-Olivier Amblard, Rémi Bardenet, Simon Barthelmé, Nicolas Tremblay (2022)

We introduce new smoothing estimators for complex signals on graphs, based on a recently studied Determinantal Point Process (DPP). These estimators are built from subsets of edges and nodes drawn according to this DPP, making up trees and unicycles, i.e., connected components containing exactly one cycle. We provide a Julia implementation of these estimators and study their performance when applied to a ranking problem. In ICASSP 2023.
extended version on arXiv:2210.08014, code on gitlab

Teaching

Some Events

Past