Training Compact Deep Learning Models for Video Classification Using Circulant Matrices. - Alexandre Araujo; Benjamin Négrevergne; Yann Chevaleyre; Jamal Atif (ECCV Workshops (4), 2018)

Theoretical evidence for adversarial robustness through randomization. - Rafael Pinot; Laurent Meunier; Alexandre Araujo; Hisashi Kashima; Florian Yger; Cédric Gouy-Pailler; Jamal Atif (NeurIPS, 2019)

On the Expressive Power of Deep Fully Circulant Neural Networks. - Alexandre Araujo; Benjamin Négrevergne; Yann Chevaleyre; Jamal Atif (CoRR, 2019)

Theoretical evidence for adversarial robustness through randomization: the case of the Exponential family. - Rafael Pinot; Laurent Meunier; Alexandre Araujo; Hisashi Kashima; Florian Yger; Cédric Gouy-Pailler; Jamal Atif (CoRR, 2019)

Robust Neural Networks using Randomized Adversarial Training. - Alexandre Araujo; Rafael Pinot; Benjamin Négrevergne; Laurent Meunier; Yann Chevaleyre; Florian Yger; Jamal Atif (CoRR, 2019)

Understanding and Training Deep Diagonal Circulant Neural Networks. - Alexandre Araujo; Benjamin Négrevergne; Yann Chevaleyre; Jamal Atif (ECAI, 2020)

Advocating for Multiple Defense Strategies Against Adversarial Examples. - Alexandre Araujo; Laurent Meunier; Rafael Pinot; Benjamin Négrevergne (PKDD/ECML Workshops, 2020)

Fast & Accurate Method for Bounding the Singular Values of Convolutional Layers with Application to Lipschitz Regularization. - Alexandre Araujo; Benjamin Négrevergne; Yann Chevaleyre; Jamal Atif (CoRR, 2020)

On Lipschitz Regularization of Convolutional Layers using Toeplitz Matrix Theory. - Alexandre Araujo; Benjamin Négrevergne; Yann Chevaleyre; Jamal Atif (AAAI, 2021)

Building Compact and Robust Deep Neural Networks with Toeplitz Matrices. - Alexandre Araujo (CoRR, 2021)

Scalable Lipschitz Residual Networks with Convex Potential Flows. - Laurent Meunier; Blaise Delattre; Alexandre Araujo; Alexandre Allauzen (CoRR, 2021)

Building Compact and Robust Deep Neural Networks with Toeplitz Matrices. (Construire des réseaux neuronaux profonds compacts et robustes avec des matrices Toeplitz). - Alexandre Araujo (2021)

A Dynamical System Perspective for Lipschitz Neural Networks. - Laurent Meunier; Blaise Delattre; Alexandre Araujo; Alexandre Allauzen (ICML, 2022)

Towards Evading the Limits of Randomized Smoothing: A Theoretical Analysis. - Raphael Ettedgui; Alexandre Araujo; Rafael Pinot; Yann Chevaleyre; Jamal Atif (CoRR, 2022)

A Unified Algebraic Perspective on Lipschitz Neural Networks. - Alexandre Araujo; Aaron J. Havens; Blaise Delattre; Alexandre Allauzen; Bin Hu 002 (ICLR, 2023)

Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration. - Blaise Delattre; Quentin Barthélemy; Alexandre Araujo; Alexandre Allauzen (ICML, 2023)

Certification of Deep Learning Models for Medical Image Segmentation. - Othmane Laousy; Alexandre Araujo; Guillaume Chassagnon; Nikos Paragios; Marie-Pierre Revel; Maria Vakalopoulou (MICCAI (4), 2023)

Exploiting Connections between Lipschitz Structures for Certifiably Robust Deep Equilibrium Models. - Aaron J. Havens; Alexandre Araujo; Siddharth Garg; Farshad Khorrami; Bin Hu 002 (NeurIPS, 2023)

Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability. - Haotian Xue 002; Alexandre Araujo; Bin Hu 002; Yongxin Chen (NeurIPS, 2023)

Towards better certified segmentation via diffusion models. - Othmane Laousy; Alexandre Araujo; Guillaume Chassagnon; Marie-Pierre Revel; Siddharth Garg; Farshad Khorrami; Maria Vakalopoulou (UAI, 2023)

R-LPIPS: An Adversarially Robust Perceptual Similarity Metric. - Sara Ghazanfari; Siddharth Garg; Prashanth Krishnamurthy; Farshad Khorrami; Alexandre Araujo (CoRR, 2023)

Towards Real-World Focus Stacking with Deep Learning. - Alexandre Araujo; Jean Ponce; Julien Mairal (CoRR, 2023)

The Lipschitz-Variance-Margin Tradeoff for Enhanced Randomized Smoothing. - Blaise Delattre; Alexandre Araujo; Quentin Barthélemy; Alexandre Allauzen (ICLR, 2024)

LipSim: A Provably Robust Perceptual Similarity Metric. - Sara Ghazanfari; Alexandre Araujo; Prashanth Krishnamurthy; Farshad Khorrami; Siddharth Garg (ICLR, 2024)

Novel Quadratic Constraints for Extending LipSDP beyond Slope-Restricted Activations. - Patricia Pauli; Aaron J. Havens; Alexandre Araujo; Siddharth Garg; Farshad Khorrami; Frank Allgöwer; Bin Hu 002 (ICLR, 2024)

On the Scalability and Memory Efficiency of Semidefinite Programs for Lipschitz Constant Estimation of Neural Networks. - Zi Wang; Bin Hu 002; Aaron J. Havens; Alexandre Araujo; Yang Zheng 001; Yudong Chen; Somesh Jha (ICLR, 2024)

Fine-grained Local Sensitivity Analysis of Standard Dot-Product Self-Attention. - Aaron J. Havens; Alexandre Araujo; Huan Zhang; Bin Hu 002 (ICML, 2024)

PAL: Proxy-Guided Black-Box Attack on Large Language Models. - Chawin Sitawarin; Norman Mu; David A. Wagner 001; Alexandre Araujo (CoRR, 2024)

Stronger Universal and Transferable Attacks by Suppressing Refusals. - David Huang; Avidan Shah; Alexandre Araujo; David A. Wagner 001; Chawin Sitawarin (NAACL (Long Papers), 2025)

EMMA: Efficient Visual Alignment in Multi-Modal LLMs. - Sara Ghazanfari; Alexandre Araujo; Prashanth Krishnamurthy; Siddharth Garg; Farshad Khorrami (Trans. Mach. Learn. Res., 2025)