35 citations · 169 across the 28 of their papers we have counts for
13 papers · 1 filter
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…
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…
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…
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…
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…
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…
Geometry-aware stationary subspace analysis
Inbal Horev, Florian Yger, Masashi Sugiyama
In many real-world applications data exhibits non-stationarity, i.e., its distribution changes over time. One approach to handling non-stationarity is to remove or minimize it befo…
Supervised LogEuclidean Metric Learning for Symmetric Positive Definite Matrices
Florian Yger, Masashi Sugiyama
Metric learning has been shown to be highly effective to improve the performance of nearest neighbor classification. In this paper, we address the problem of metric learning for Sy…
Adaptive Canonical Correlation Analysis Based On Matrix Manifolds
Florian Yger, Maxime Berar, Gilles Gasso +1
In this paper, we formulate the Canonical Correlation Analysis (CCA) problem on matrix manifolds. This framework provides a natural way for dealing with matrix constraints and tool…