35 citations · 169 across the 28 of their papers we have counts for
29 papers
Generative Augmentation for EEG Motor Imagery Classification: A Class-Conditional VAE with Cycle-Consistent Decoder Refinement
Matei Moldoveanu, Alain Sirois, Claire Ben Ali +2
We investigate whether a generative model can supply useful synthetic motor-imagery (MI) electroencephalography (EEG) trials that improve the accuracy of independent downstream cla…
A probabilistic view on Riemannian machine learning models for SPD matrices
Thibault de Surrel, Florian Yger, Fabien Lotte +1
The goal of this paper is to show how different machine learning tools on the Riemannian manifold of Symmetric Positive Definite (SPD) matrices can be united under…
Wrapped Gaussian on the manifold of Symmetric Positive Definite Matrices
Thibault de Surrel, Fabien Lotte, Sylvain Chevallier +1
Circular and non-flat data distributions are prevalent across diverse domains of data science, yet their specific geometric structures often remain underutilized in machine learnin…
Meta-survey on outlier and anomaly detection
Madalina Olteanu, Fabrice Rossi, Florian Yger
The impact of outliers and anomalies on model estimation and data processing is of paramount importance, as evidenced by the extensive body of research spanning various fields over…
Challenges in anomaly and change point detection
Madalina Olteanu, Fabrice Rossi, Florian Yger
This paper presents an introduction to the state-of-the-art in anomaly and change-point detection. On the one hand, the main concepts needed to understand the vast scientific liter…
Is the U-Net Directional-Relationship Aware?
Mateus Riva, Pietro Gori, Florian Yger +1
CNNs are often assumed to be capable of using contextual information about distinct objects (such as their directional relations) inside their receptive field. However, the nature…
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…
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…
Estimating Individual Treatment Effects through Causal Populations Identification
Céline Beji, Michaël Bon, Florian Yger +1
Estimating the Individual Treatment Effect from observational data, defined as the difference between outcomes with and without treatment or intervention, while observing just one…
A unified view on differential privacy and robustness to adversarial examples
Rafael Pinot, Florian Yger, Cédric Gouy-Pailler +1
This short note highlights some links between two lines of research within the emerging topic of trustworthy machine learning: differential privacy and robustness to adversarial ex…
Robust Neural Networks using Randomized Adversarial Training
Alexandre Araujo, Laurent Meunier, Rafael Pinot +1
This paper tackles the problem of defending a neural network against adversarial attacks crafted with different norms (in particular and bounded adversarial…
Theoretical evidence for adversarial robustness through randomization
Rafael Pinot, Laurent Meunier, Alexandre Araujo +4
This paper investigates the theory of robustness against adversarial attacks. It focuses on the family of randomization techniques that consist in injecting noise in the network at…
Graph-based Clustering under Differential Privacy
Rafael Pinot, Anne Morvan, Florian Yger +2
In this paper, we present the first differentially private clustering method for arbitrary-shaped node clusters in a graph. This algorithm takes as input only an approximate Minimu…
Uplift Modeling from Separate Labels
Ikko Yamane, Florian Yger, Jamal Atif +1
Uplift modeling is aimed at estimating the incremental impact of an action on an individual's behavior, which is useful in various application domains such as targeted marketing (a…