| Square attack: a query-efficient black-box adversarial attack via random search M Andriushchenko, F Croce, N Flammarion, M Hein ECCV, 2020 | 1493 | 2020 |
| Robustbench: a standardized adversarial robustness benchmark F Croce, M Andriushchenko, V Sehwag, E Debenedetti, N Flammarion, ... arXiv preprint arXiv:2010.09670, 2020 | 1049 | 2020 |
| Jailbreakbench: An open robustness benchmark for jailbreaking large language models P Chao, E Debenedetti, A Robey, M Andriushchenko, F Croce, V Sehwag, ... Advances in Neural Information Processing Systems 37, 55005-55029, 2024 | 475 | 2024 |
| Understanding and Improving Fast Adversarial Training M Andriushchenko, N Flammarion Advances in Neural Information Processing Systems, 2020 | 449 | 2020 |
| Jailbreaking leading safety-aligned llms with simple adaptive attacks M Andriushchenko, F Croce, N Flammarion arXiv preprint arXiv:2404.02151, 2024 | 368 | 2024 |
| Sampling can be faster than optimization YA Ma, Y Chen, C Jin, N Flammarion, MI Jordan Proceedings of the National Academy of Sciences 116 (42), 20881-20885, 2019 | 271 | 2019 |
| Towards understanding sharpness-aware minimization M Andriushchenko, N Flammarion International conference on machine learning, 639-668, 2022 | 227 | 2022 |
| Is there an analog of Nesterov acceleration for gradient-based MCMC? YA Ma, NS Chatterji, X Cheng, N Flammarion, PL Bartlett, MI Jordan | 215 | 2021 |
| Harder, better, faster, stronger convergence rates for least-squares regression A Dieuleveut, N Flammarion, F Bach Journal of Machine Learning Research 18 (101), 1-51, 2017 | 209 | 2017 |
| From averaging to acceleration, there is only a step-size N Flammarion, F Bach Conference on learning theory, 658-695, 2015 | 167 | 2015 |
| Sparse-rs: a versatile framework for query-efficient sparse black-box adversarial attacks F Croce, M Andriushchenko, ND Singh, N Flammarion, M Hein Proceedings of the AAAI conference on artificial intelligence 36 (6), 6437-6445, 2022 | 158 | 2022 |
| Implicit bias of sgd for diagonal linear networks: a provable benefit of stochasticity S Pesme, L Pillaud-Vivien, N Flammarion Advances in Neural Information Processing Systems 34, 29218-29230, 2021 | 154 | 2021 |
| Averaging stochastic gradient descent on Riemannian manifolds N Tripuraneni, N Flammarion, F Bach, MI Jordan Conference On Learning Theory, 2018 | 142 | 2018 |
| On the effectiveness of adversarial training against common corruptions K Kireev, M Andriushchenko, N Flammarion Uncertainty in Artificial Intelligence, 1012-1021, 2022 | 139 | 2022 |
| On the theory of variance reduction for stochastic gradient Monte Carlo NS Chatterji, N Flammarion, YA Ma, PL Bartlett, MI Jordan International Conference on Machine Learning, 764--773, 2018 | 115 | 2018 |
| A modern look at the relationship between sharpness and generalization M Andriushchenko, F Croce, M Müller, M Hein, N Flammarion International Conference on Machine Learning 202, 840--902, 2023 | 103 | 2023 |
| Improved bounds for discretization of Langevin diffusions: Near-optimal rates without convexity W Mou, N Flammarion, MJ Wainwright, PL Bartlett Bernoulli 28 (3), 1577-1601, 2022 | 98 | 2022 |
| Fast mean estimation with sub-Gaussian rates Y Cherapanamjeri, N Flammarion, PL Bartlett Conference on Learning Theory, 786--806, 2019 | 98 | 2019 |
| Gradient flow dynamics of shallow relu networks for square loss and orthogonal inputs E Boursier, L Pillaud-Vivien, N Flammarion Advances in Neural Information Processing Systems 35, 20105-20118, 2022 | 97 | 2022 |
| Escaping from saddle points on Riemannian manifolds Y Sun, N Flammarion, M Fazel Advances in Neural Information Processing Systems, 7276-7286, 2019 | 97 | 2019 |
Maksym AndriushchenkoELLIS Institute Tübingen & Max Planck Institute for Intelligent SystemsVerified email at epfl.ch
