activity
20122022
most citedTheoretical evidence for adversarial robustness through randomization

35 citations · 79 across the 13 of their papers we have counts for

collaborators

17 papers

cs.AI2022

Multi-winner Approval Voting Goes Epistemic

Tahar Allouche, Jérôme Lang, Florian Yger

Epistemic voting interprets votes as noisy signals about a ground truth. We consider contexts where the truth consists of a set of objective winners, knowing a lower and upper boun…

cs.CV20211 cited

The Minimum Edit Arborescence Problem and Its Use in Compressing Graph Collections [Extended Version]

Lucas Gnecco, Nicolas Boria, Sébastien Bougleux +2

The inference of minimum spanning arborescences within a set of objects is a general problem which translates into numerous application-specific unsupervised learning tasks. We int…

stat.ML2021

Template-Based Graph Clustering

Mateus Riva, Florian Yger, Pietro Gori +2

We propose a novel graph clustering method guided by additional information on the underlying structure of the clusters (or communities). The problem is formulated as the matching…

cs.LG2021

Scaling up graph homomorphism for classification via sampling

Paul Beaujean, Florian Sikora, Florian Yger

Feature generation is an open topic of investigation in graph machine learning. In this paper, we study the use of graph homomorphism density features as a scalable alternative to…

eess.SP20211 cited

RIGOLETTO -- RIemannian GeOmetry LEarning: applicaTion To cOnnectivity. A contribution to the Clinical BCI Challenge -- WCCI2020

Marie-Constance Corsi, Florian Yger, Sylvain Chevallier +1

This short technical report describes the approach submitted to the Clinical BCI Challenge-WCCI2020. This submission aims to classify motor imagery task from EEG signals and relies…

cs.CV20211 cited

Approximation of dilation-based spatial relations to add structural constraints in neural networks

Mateus Riva, Pietro Gori, Florian Yger +2

Spatial relations between objects in an image have proved useful for structural object recognition. Structural constraints can act as regularization in neural network training, imp…