35 citations · 169 across the 28 of their papers we have counts for
11 papers · 1 filter
Non parametric estimation of causal populations in a counterfactual scenario
Celine Beji, Florian Yger, Jamal Atif
In causality, estimating the effect of a treatment without confounding inference remains a major issue because requires to assess the outcome in both case with and without treatmen…
Truth-tracking via Approval Voting: Size Matters
Tahar Allouche, Jérôme Lang, Florian Yger
Epistemic social choice aims at unveiling a hidden ground truth given votes, which are interpreted as noisy signals about it. We consider here a simple setting where votes consist…
A new Sinkhorn algorithm with Deletion and Insertion operations
Luc Brun, Benoit Gaüzère, Sébastien Bougleux +1
This technical report is devoted to the continuous estimation of an epsilon-assignment. Roughly speaking, an epsilon assignment between two sets V1 and V2 may be understood as a bi…
Functional connectivity ensemble method to enhance BCI performance (FUCONE)
Marie-Constance Corsi, Sylvain Chevallier, Fabrizio De Vico Fallani +1
Functional connectivity is a key approach to investigate oscillatory activities of the brain that provides important insights on the underlying dynamic of neuronal interactions and…
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…
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…
Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences
Ikko Yamane, Junya Honda, Florian Yger +1
Ordinary supervised learning is useful when we have paired training data of input and output . However, such paired data can be difficult to collect in practice. In this pap…
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…
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…
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…
On the robustness of randomized classifiers to adversarial examples
Rafael Pinot, Laurent Meunier, Florian Yger +3
This paper investigates the theory of robustness against adversarial attacks. We focus on randomized classifiers (\emph{i.e.} classifiers that output random variables) and provide…