Alexandre Araujo

alexandre.araujonyu.edu

Last update November 2023

I am currently a postdoctoral researcher at New York University in the EnSuRe Research Group. I work with Siddharth Garg and Farshad Khorrami on Trustworthy Machine Learning. Prior to that, I was a postdoctoral researcher at INRIA and École Normale Supérieure (ENS) in the WILLOW project-team in Paris, France. I obtained my PhD in Computer Science in June 2021 at Université Paris Dauphine-PSL where I was advised by Pr. Jamal Atif, Pr. Yann Chevaleyre and Dr. Benjamin Negrevergne.
Research
LipSim: A Provably Robust Perceptual Similarity Metric
S. Ghazanfari, A. Araujo, P. Krishnamurthy, F. Khorrami and S. Garg
preprint • 2023
papercodebibtex
The Lipschitz-Variance-Margin Tradeoff for Enhanced Randomized Smoothing
B. Delattre, A. Araujo, Q. Barthélemy and A. Allauzen
Preprint • 2023
paperbibtex
Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability
H. Xue, A. Araujo, B. Hu and Y. Chen
NeurIPS • 2023
papercodebibtex
Exploiting Connections between Lipschitz Structures for Certifiably Robust Deep Equilibrium Models
A. Havens, A. Araujo, S. Garg, F. Khorrami and B. Hu
NeurIPS • 2023
papercodebibtex
R-LPIPS: An Adversarially Robust Perceptual Similarity Metric
S. Ghazanfari, S. Garg, P. Krishnamurthy, F. Khorrami and A. Araujo
ICML Workshop on New Frontiers in Adversarial Machine Learning • 2023
papercodebibtex
Certification of Deep Learning Models for Medical Image Segmentation
O. Laousy, A. Araujo, G. Chassagnon, N. Paragios, M. Revel and M. Vakalopoulou
MICCAI • 2023
paperbibtex
Towards Better Certified Segmentation via Diffusion Models
O. Laousy, A. Araujo, G. Chassagnon, M. Revel, S. Garg, F. Khorrami and M. Vakalopoulou
UAI • 2023
papercodebibtex
Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration
B. Delattre, Q. Barthelemy, A. Araujo and A. Allauzen
ICML • 2023
papercodebibtex
A Unified Algebraic Perspective on Lipschitz Neural Networks
A. Araujo*, A. Havens*, B. Delattre, A. Allauzen and B. Hu
ICLR • 2023 — Spotlight
paperbibtex
Towards Evading the Limits of Randomized Smoothing: A Theoretical Analysis
R. Ettedgui*, A. Araujo*, R. Pinot, Y. Chevaleyre and J. Atif
Preprint • 2022
paperbibtex
A Dynamical System Perspective for Lipschitz Neural Networks
L. Meunier*, B. Delattre*, A. Araujo* and A. Allauzen
ICML • 2022 — ORAL
paperbibtex
Building Compact and Robust Deep Neural Networks with Toeplitz Matrices
A. Araujo
PhD Thesis • 2021
paperbibtex
On Lipschitz Regularization of Convolutional Layers using Toeplitz Matrix Theory
A. Araujo, B. Negrevergne, Y. Chevaleyre and J. Atif
AAAI • 2020
papercodebibtex
Advocating for Multiple Defense Strategies against Adversarial Examples
A. Araujo, L. Menuier, R. Pinot and B. Negrevergne
ECML - Workshops • 2020
paperbibtex
Theoretical Evidence for Adversarial Robustness through Randomization
R. Pinot, L. Meunier, A. Araujo, H. Kashima, F. Yger, C. Gouy-Pailler and J. Atif
NeurIPS • 2019
papercodebibtex
Understanding and Training Deep Diagonal Circulant Neural Networks
A. Araujo, B. Negrevergne, Y. Chevaleyre and J. Atif
ECAI • 2019
paperbibtex
Training Compact Deep Learning Models for Video Classification using Circulant Matrices
A. Araujo, B. Negrevergne, Y. Chevaleyre and J. Atif
ECCV - Workshops • 2018
papercodebibtex