| Theoretical evidence for adversarial robustness through randomization R Pinot, L Meunier, A Araujo, H Kashima, F Yger, C Gouy-Pailler, J Atif Neural Information Processing Systems (NeurIPS), 2019 | 105 | 2019 |
| A Unified Algebraic Perspective on Lipschitz Neural Networks A Araujo, AJ Havens, B Delattre, A Allauzen, B Hu International Conference on Learning Representations (ICLR), 2023 | 45 | 2023 |
| A Dynamical System Perspective for Lipschitz Neural Networks L Meunier, B Delattre, A Araujo, A Allauzen International Conference on Machine Learning (ICML), 2022 | 44 | 2022 |
| Robust neural networks using randomized adversarial training A Araujo, L Meunier, R Pinot, B Negrevergne arXiv preprint arXiv:1903.10219, 2019 | 37 | 2019 |
| On Lipschitz Regularization of Convolutional Layers using Toeplitz Matrix Theory A Araujo, B Negrevergne, Y Chevaleyre, J Atif Proceedings of the AAAI Conference on Artificial Intelligence, 2020 | 32 | 2020 |
| Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability H Xue, A Araujo, B Hu, Y Chen Neural Information Processing Systems (NeurIPS), 2023 | 27 | 2023 |
| Training compact deep learning models for video classification using circulant matrices A Araujo, B Negrevergne, Y Chevaleyre, J Atif The European Conference on Computer Vision (ECCV) Workshops, 2018 | 19 | 2018 |
| Pal: Proxy-guided black-box attack on large language models C Sitawarin, N Mu, D Wagner, A Araujo arXiv preprint arXiv:2402.09674, 2024 | 16 | 2024 |
| R-LPIPS: An Adversarially Robust Perceptual Similarity Metric S Ghazanfari, S Garg, P Krishnamurthy, F Khorrami, A Araujo 2nd ICML Workshop on New Frontiers in Adversarial Machine Learning, 2023 | 16 | 2023 |
| Advocating for multiple defense strategies against adversarial examples A Araujo, L Meunier, R Pinot, B Negrevergne ECML PKDD 2020 Workshops, 2020 | 11 | 2020 |
| Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration B Delattre, Q Barthélemy, A Araujo, A Allauzen International Conference on Machine Learning (ICML), 2023 | 8 | 2023 |
| Novel Quadratic Constraints for Extending LipSDP beyond Slope-Restricted Activations P Pauli, A Havens, A Araujo, S Garg, F Khorrami, F Allgöwer, B Hu International Conference on Learning Representations (ICLR), 2024 | 5 | 2024 |
| On the scalability and memory efficiency of semidefinite programs for Lipschitz constant estimation of neural networks Z Wang, AJ Havens, A Araujo, Y Zheng, B Hu, Y Chen, S Jha International Conference on Learning Representations (ICLR), 2024 | 5 | 2024 |
| Exploiting Connections between Lipschitz Structures for Certifiably Robust Deep Equilibrium Models AJ Havens, A Araujo, S Garg, F Khorrami, B Hu Neural Information Processing Systems (NeurIPS), 2023 | 5 | 2023 |
| Towards Better Certified Segmentation via Diffusion Models O Laousy, A Araujo, G Chassagnon, MP Revel, S Garg, F Khorrami, ... Conference on Uncertainty in Artificial Intelligence (UAI), 2023 | 4 | 2023 |
| Understanding and Training Deep Diagonal Circulant Neural Networks A Araujo, B Negrevergne, Y Chevaleyre, J Atif European Conference on Artificial Intelligence (ECAI), 2019 | 4* | 2019 |
| LipSim: A Provably Robust Perceptual Similarity Metric S Ghazanfari, A Araujo, P Krishnamurthy, F Khorrami, S Garg International Conference on Learning Representations (ICLR), 2024 | 3 | 2024 |
| The Lipschitz-Variance-Margin Tradeoff for Enhanced Randomized Smoothing B Delattre, A Araujo, Q Barthélemy, A Allauzen International Conference on Learning Representations (ICLR), 2024 | 3 | 2024 |
| Towards Real-World Focus Stacking with Deep Learning A Araujo, J Ponce, J Mairal arXiv preprint arXiv:2311.17846, 2023 | 2 | 2023 |
| Towards evading the limits of randomized smoothing: A theoretical analysis R Ettedgui, A Araujo, R Pinot, Y Chevaleyre, J Atif arXiv preprint arXiv:2206.01715, 2022 | 2 | 2022 |
Laurent MeunierMILES Team, LAMSADE Université Paris DauphineVerified email at dauphine.eu
