11 citations · 13 across the 5 of their papers we have counts for
5 papers
Robust Consensus in Ranking Data Analysis: Definitions, Properties and Computational Issues
Morgane Goibert, Clément Calauzènes, Ekhine Irurozki +1
As the issue of robustness in AI systems becomes vital, statistical learning techniques that are reliable even in presence of partly contaminated data have to be developed. Prefere…
An Adversarial Robustness Perspective on the Topology of Neural Networks
Morgane Goibert, Thomas Ricatte, Elvis Dohmatob
In this paper, we investigate the impact of neural networks (NNs) topology on adversarial robustness. Specifically, we study the graph produced when an input traverses all the laye…
Origins of Low-dimensional Adversarial Perturbations
Elvis Dohmatob, Chuan Guo, Morgane Goibert
In this paper, we initiate a rigorous study of the phenomenon of low-dimensional adversarial perturbations (LDAPs) in classification. Unlike the classical setting, these perturbati…
Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications
Morgane Goibert, Stéphan Clémençon, Ekhine Irurozki +1
The concept of median/consensus has been widely investigated in order to provide a statistical summary of ranking data, i.e. realizations of a random permutation of a finite se…
Adversarial Robustness via Label-Smoothing
Morgane Goibert, Elvis Dohmatob
We study Label-Smoothing as a means for improving adversarial robustness of supervised deep-learning models. After establishing a thorough and unified framework, we propose several…