35 citations · 83 across the 11 of their papers we have counts for
12 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…
Streaming Binary Sketching based on Subspace Tracking and Diagonal Uniformization
Anne Morvan, Antoine Souloumiac, Cédric Gouy-Pailler +1
In this paper, we address the problem of learning compact similarity-preserving embeddings for massive high-dimensional streams of data in order to perform efficient similarity sea…
Graph sketching-based Space-efficient Data Clustering
Anne Morvan, Krzysztof Choromanski, Cédric Gouy-Pailler +1
In this paper, we address the problem of recovering arbitrary-shaped data clusters from datasets while facing \emph{high space constraints}, as this is for instance the case in man…
Structured adaptive and random spinners for fast machine learning computations
Mariusz Bojarski, Anna Choromanska, Krzysztof Choromanski +6
We consider an efficient computational framework for speeding up several machine learning algorithms with almost no loss of accuracy. The proposed framework relies on projections v…
Multi-dimensional signal approximation with sparse structured priors using split Bregman iterations
Yoann Isaac, Quentin Barthélemy, Cédric Gouy-Pailler +2
This paper addresses the structurally-constrained sparse decomposition of multi-dimensional signals onto overcomplete families of vectors, called dictionaries. The contribution of…
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…
Multivariate Temporal Dictionary Learning for EEG
Quentin Barthélemy, Cédric Gouy-Pailler, Yoann Isaac +3
This article addresses the issue of representing electroencephalographic (EEG) signals in an efficient way. While classical approaches use a fixed Gabor dictionary to analyze EEG s…
Multi-dimensional sparse structured signal approximation using split Bregman iterations
Yoann Isaac, Quentin Barthélemy, Jamal Atif +2
The paper focuses on the sparse approximation of signals using overcomplete representations, such that it preserves the (prior) structure of multi-dimensional signals. The underlyi…