96 citations · 152 across the 20 of their papers we have counts for
9 papers · 1 filter
Reward-Preserving Attacks For Robust Reinforcement Learning
Lucas Schott, Elies Gherbi, Hatem Hajri +1
Adversarial training in reinforcement learning (RL) is challenging because perturbations cascade through trajectories and compound over time, making fixed-strength attacks either o…
Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey
Lucas Schott, Josephine Delas, Hatem Hajri +5
Deep Reinforcement Learning (DRL) is a subfield of machine learning for training autonomous agents that take sequential actions across complex environments. Despite its significant…
SEEDS: Exponential SDE Solvers for Fast High-Quality Sampling from Diffusion Models
Martin Gonzalez, Nelson Fernandez, Thuy Tran +3
A potent class of generative models known as Diffusion Probabilistic Models (DPMs) has become prominent. A forward diffusion process adds gradually noise to data, while a model lea…
Noisy Learning for Neural ODEs Acts as a Robustness Locus Widening
Martin Gonzalez, Hatem Hajri, Loic Cantat +1
We investigate the problems and challenges of evaluating the robustness of Differential Equation-based (DE) networks against synthetic distribution shifts. We propose a novel and s…
Riemannian data-dependent randomized smoothing for neural networks certification
Pol Labarbarie, Hatem Hajri, Marc Arnaudon
Certification of neural networks is an important and challenging problem that has been attracting the attention of the machine learning community since few years. In this paper, we…
Improving Robustness of Deep Reinforcement Learning Agents: Environment Attack based on the Critic Network
Lucas Schott, Hatem Hajri, Sylvain Lamprier
To improve policy robustness of deep reinforcement learning agents, a line of recent works focus on producing disturbances of the environment. Existing approaches of the literature…
Stochastic sparse adversarial attacks
Manon Césaire, Lucas Schott, Hatem Hajri +2
This paper introduces stochastic sparse adversarial attacks (SSAA), standing as simple, fast and purely noise-based targeted and untargeted attacks of neural network classifiers (N…
Geomstats: A Python Package for Riemannian Geometry in Machine Learning
Nina Miolane, Alice Le Brigant, Johan Mathe +16
We introduce Geomstats, an open-source Python toolbox for computations and statistics on nonlinear manifolds, such as hyperbolic spaces, spaces of symmetric positive definite matri…
From Node Embedding To Community Embedding : A Hyperbolic Approach
Thomas Gerald, Hadi Zaatiti, Hatem Hajri +2
Detecting communities on graphs has received significant interest in recent literature. Current state-of-the-art community embedding approach called \textit{ComE} tackles this prob…