activity
20152022
most citedTheoretical evidence for adversarial robustness through randomization

35 citations · 63 across the 9 of their papers we have counts for

collaborators

26 papers

cs.LG20222 cited

Towards Consistency in Adversarial Classification

Laurent Meunier, Raphaël Ettedgui, Rafael Pinot +2

In this paper, we study the problem of consistency in the context of adversarial examples. Specifically, we tackle the following question: can surrogate losses still be used as a p…

cs.IR20211 cited

Two-sided fairness in rankings via Lorenz dominance

Virginie Do, Sam Corbett-Davies, Jamal Atif +1

We consider the problem of generating rankings that are fair towards both users and item producers in recommender systems. We address both usual recommendation (e.g., of music or m…

cs.LG2021

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…

cs.GT20212 cited

Mixed Nash Equilibria in the Adversarial Examples Game

Laurent Meunier, Meyer Scetbon, Rafael Pinot +2

This paper tackles the problem of adversarial examples from a game theoretic point of view. We study the open question of the existence of mixed Nash equilibria in the zero-sum gam…

cs.LG2020

AAMDRL: Augmented Asset Management with Deep Reinforcement Learning

Eric Benhamou, David Saltiel, Sandrine Ungari +2

Can an agent learn efficiently in a noisy and self adapting environment with sequential, non-stationary and non-homogeneous observations? Through trading bots, we illustrate how De…

q-fin.PM2020

Detecting and adapting to crisis pattern with context based Deep Reinforcement Learning

Eric Benhamou, David Saltiel, Jean-Jacques Ohana +1

Deep reinforcement learning (DRL) has reached super human levels in complex tasks like game solving (Go and autonomous driving). However, it remains an open question whether DRL ca…