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07/20/2023
Adversarial attacks for mixtures of classifiers
Mixtures of classifiers (a.k.a. randomized ensembles) have been proposed...
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05/30/2023
Precision-Recall Divergence Optimization for Generative Modeling with GANs and Normalizing Flows
Achieving a balance between image quality (precision) and diversity (rec...
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02/14/2023
Randomization for adversarial robustness: the Good, the Bad and the Ugly
Deep neural networks are known to be vulnerable to adversarial attacks: ...
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02/01/2023
Training Normalizing Flows with the Precision-Recall Divergence
Generative models can have distinct mode of failures like mode dropping ...
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06/03/2022
Towards Evading the Limits of Randomized Smoothing: A Theoretical Analysis
Randomized smoothing is the dominant standard for provable defenses agai...
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06/01/2022
An α-No-Regret Algorithm For Graphical Bilinear Bandits
We propose the first regret-based approach to the Graphical Bilinear Ban...
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05/20/2022
Towards Consistency in Adversarial Classification
In this paper, we study the problem of consistency in the context of adv...
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03/02/2022
Reactive Stepping for Humanoid Robots using Reinforcement Learning: Application to Standing Push Recovery on the Exoskeleton Atalante
State-of-the-art reinforcement learning is now able to learn versatile l...
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08/10/2021
Asymptotic convergence rates for averaging strategies
Parallel black box optimization consists in estimating the optimum of a ...
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07/15/2021
On the expressivity of bi-Lipschitz normalizing flows
An invertible function is bi-Lipschitz if both the function and its inve...
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02/22/2021
On the robustness of randomized classifiers to adversarial examples
This paper investigates the theory of robustness against adversarial att...
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02/13/2021
Mixed Nash Equilibria in the Adversarial Examples Game
This paper tackles the problem of adversarial examples from a game theor...
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12/14/2020
Best Arm Identification in Graphical Bilinear Bandits
We introduce a new graphical bilinear bandit problem where a learner (or...
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06/15/2020
Fast Accurate Method for Bounding the Singular Values of Convolutional Layers with Application to Lipschitz Regularization
This paper tackles the problem of Lipschitz regularization of Convolutio...
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04/24/2020
Proving μ>1
Choosing the right selection rate is a long standing issue in evolutiona...
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02/26/2020
Randomization matters. How to defend against strong adversarial attacks
Is there a classifier that ensures optimal robustness against all advers...
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10/01/2019
Online Trajectory Planning Through Combined Trajectory Optimization and Function Approximation: Application to the Exoskeleton Atalante
Autonomous robots require online trajectory planning capability to opera...
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03/25/2019
Robust Neural Networks using Randomized Adversarial Training
Since the discovery of adversarial examples in machine learning, researc...
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01/29/2019
On the Expressive Power of Deep Fully Circulant Neural Networks
In this paper, we study deep fully circulant neural networks, that is de...
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10/02/2018
Training compact deep learning models for video classification using circulant matrices
In real world scenarios, model accuracy is hardly the only factor to con...
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06/23/2018
Disease Classification in Metagenomics with 2D Embeddings and Deep Learning
Deep learning (DL) techniques have shown unprecedented success when appl...
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12/01/2017
Deep Learning for Metagenomic Data: using 2D Embeddings and Convolutional Neural Networks
Deep learning (DL) techniques have had unprecedented success when applie...
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11/15/2011