35 citations · 38 across the 3 of their papers we have counts for
7 papers
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…
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…
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…
On the Needs for Rotations in Hypercubic Quantization Hashing
Anne Morvan, Antoine Souloumiac, Krzysztof Choromanski +2
The aim of this paper is to endow the well-known family of hypercubic quantization hashing methods with theoretical guarantees. In hypercubic quantization, applying a suitable (ran…
TripleSpin - a generic compact paradigm for fast machine learning computations
Krzysztof Choromanski, Francois Fagan, Cedric Gouy-Pailler +3
We present a generic compact computational framework relying on structured random matrices that can be applied to speed up several machine learning algorithms with almost no loss o…