Alexandre Araujo

alexandre.araujonyu.edu

Last update February 2024

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
PAL: Proxy-Guided Black-Box Attack on Large Language Models
C. Sitawarin, N. Mu, D. Wagner and A. Araujo
preprint • 2024
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LipSim: A Provably Robust Perceptual Similarity Metric
S. Ghazanfari, A. Araujo, P. Krishnamurthy, F. Khorrami and S. Garg
ICLR • 2024
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On the Scalability and Memory Efficiency of Semidefinite Programs for Lipschitz Constant Estimation of Neural Networks
Z. Wang, A. Havens, A. Araujo, Y. Zheng, B. Hu, Y. Chen and S. Jha
ICLR • 2024
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The Lipschitz-Variance-Margin Tradeoff for Enhanced Randomized Smoothing
B. Delattre, A. Araujo, Q. Barthélemy and A. Allauzen
ICLR • 2024
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Novel Quadratic Constraints for Extending LipSDP beyond Slope-Restricted Activations
P. Pauli, A. Havens, A. Araujo, S. Garg, F. Khorrami, F. Allgower and B. Hu
ICLR • 2024
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Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability
H. Xue, A. Araujo, B. Hu and Y. Chen
NeurIPS • 2023
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Exploiting Connections between Lipschitz Structures for Certifiably Robust Deep Equilibrium Models
A. Havens, A. Araujo, S. Garg, F. Khorrami and B. Hu
NeurIPS • 2023
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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
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Certification of Deep Learning Models for Medical Image Segmentation
O. Laousy, A. Araujo, G. Chassagnon, N. Paragios, M. Revel and M. Vakalopoulou
MICCAI • 2023
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Towards Better Certified Segmentation via Diffusion Models
O. Laousy, A. Araujo, G. Chassagnon, M. Revel, S. Garg, F. Khorrami and M. Vakalopoulou
UAI • 2023
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Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration
B. Delattre, Q. Barthelemy, A. Araujo and A. Allauzen
ICML • 2023
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A Unified Algebraic Perspective on Lipschitz Neural Networks
A. Araujo*, A. Havens*, B. Delattre, A. Allauzen and B. Hu
ICLR • 2023 — Spotlight
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Towards Evading the Limits of Randomized Smoothing: A Theoretical Analysis
R. Ettedgui*, A. Araujo*, R. Pinot, Y. Chevaleyre and J. Atif
Preprint • 2022
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A Dynamical System Perspective for Lipschitz Neural Networks
L. Meunier*, B. Delattre*, A. Araujo* and A. Allauzen
ICML • 2022 — ORAL
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Building Compact and Robust Deep Neural Networks with Toeplitz Matrices
A. Araujo
PhD Thesis • 2021
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On Lipschitz Regularization of Convolutional Layers using Toeplitz Matrix Theory
A. Araujo, B. Negrevergne, Y. Chevaleyre and J. Atif
AAAI • 2020
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Advocating for Multiple Defense Strategies against Adversarial Examples
A. Araujo, L. Menuier, R. Pinot and B. Negrevergne
ECML - Workshops • 2020
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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
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Understanding and Training Deep Diagonal Circulant Neural Networks
A. Araujo, B. Negrevergne, Y. Chevaleyre and J. Atif
ECAI • 2019
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Training Compact Deep Learning Models for Video Classification using Circulant Matrices
A. Araujo, B. Negrevergne, Y. Chevaleyre and J. Atif
ECCV - Workshops • 2018
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