Alexandre Araujo

alexandre.araujonyu.edu

Last update August 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.
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Research
Semidefinite Programs for Computing Lipschitz Bounds of Neural Networks with MaxMin Activations
P. Pauli, A. Havens, A. Araujo, S. Garg, F. Khorrami, F. Allgower and B. Hu
Preprint • 2023
paper • bibtex
Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability
H. Xue, A. Araujo, B. Hu and Y. Chen
Preprint • 2023
paper • code • bibtex
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
paper • code • bibtex
Certification of Deep Learning Models for Medical Image Segmentation
O. Laousy, A. Araujo, G. Chassagnon, N. Paragios, M. Revel and M. Vakalopoulou
MICCAI • 2023
paper • bibtex
Towards Better Certified Segmentation via Diffusion Models
O. Laousy, A. Araujo, G. Chassagnon, M. Revel, S. Garg, F. Khorrami and M. Vakalopoulou
UAI • 2023
paper • code • bibtex
Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration
B. Delattre, Q. Barthelemy, A. Araujo and A. Allauzen
ICML • 2023
paper • code • bibtex
A Unified Algebraic Perspective on Lipschitz Neural Networks
A. Araujo*, A. Havens*, B. Delattre, A. Allauzen and B. Hu
ICLR • 2023 — Spotlight
paper • bibtex
Towards Evading the Limits of Randomized Smoothing: A Theoretical Analysis
R. Ettedgui*, A. Araujo*, R. Pinot, Y. Chevaleyre and J. Atif
Preprint • 2022
paper • bibtex
A Dynamical System Perspective for Lipschitz Neural Networks
L. Meunier*, B. Delattre*, A. Araujo* and A. Allauzen
ICML • 2022 — ORAL
paper • bibtex
Building Compact and Robust Deep Neural Networks with Toeplitz Matrices
A. Araujo
PhD Thesis • 2021
paper • bibtex
On Lipschitz Regularization of Convolutional Layers using Toeplitz Matrix Theory
A. Araujo, B. Negrevergne, Y. Chevaleyre and J. Atif
AAAI • 2020
paper • code • bibtex
Advocating for Multiple Defense Strategies against Adversarial Examples
A. Araujo, L. Menuier, R. Pinot and B. Negrevergne
ECML - Workshops • 2020
paper • bibtex
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
paper • code • bibtex
Understanding and Training Deep Diagonal Circulant Neural Networks
A. Araujo, B. Negrevergne, Y. Chevaleyre and J. Atif
ECAI • 2019
paper • bibtex
Training Compact Deep Learning Models for Video Classification using Circulant Matrices
A. Araujo, B. Negrevergne, Y. Chevaleyre and J. Atif
ECCV - Workshops • 2018
paper • code • bibtex