| Square attack: a query-efficient black-box adversarial attack via random search M Andriushchenko, F Croce, N Flammarion, M Hein ECCV, 2020 | 296 | 2020 |
| Robustbench: a standardized adversarial robustness benchmark F Croce, M Andriushchenko, V Sehwag, E Debenedetti, N Flammarion, ... arXiv preprint arXiv:2010.09670, 2020 | 136 | 2020 |
| From averaging to acceleration, there is only a step-size N Flammarion, F Bach Conference on Learning Theory, 658-695, 2015 | 136 | 2015 |
| Harder, better, faster, stronger convergence rates for least-squares regression A Dieuleveut, N Flammarion, F Bach The Journal of Machine Learning Research 18 (1), 3520-3570, 2017 | 123 | 2017 |
| 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 | 107 | 2019 |
| Understanding and Improving Fast Adversarial Training M Andriushchenko, N Flammarion Advances in Neural Information Processing Systems, 2020 | 97 | 2020 |
| Is there an analog of Nesterov acceleration for gradient-based MCMC? YA Ma, NS Chatterji, X Cheng, N Flammarion, PL Bartlett, MI Jordan Bernoulli 27 (3), 1942-1992, 2021 | 86* | 2021 |
| 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 | 84 | 2018 |
| Averaging stochastic gradient descent on Riemannian manifolds N Tripuraneni, N Flammarion, F Bach, MI Jordan Conference On Learning Theory, 2018 | 72 | 2018 |
| Optimal rates of statistical seriation N Flammarion, C Mao, P Rigollet Bernoulli 25 (1), 623-653, 2019 | 57 | 2019 |
| Fast mean estimation with sub-Gaussian rates Y Cherapanamjeri, N Flammarion, PL Bartlett Conference on Learning Theory, 786--806, 2019 | 48 | 2019 |
| Escaping from saddle points on Riemannian manifolds Y Sun, N Flammarion, M Fazel Advances in Neural Information Processing Systems, 7276-7286, 2019 | 41 | 2019 |
| 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 | 27 | 2022 |
| 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 | 26 | 2022 |
| Stochastic Composite Least-Squares Regression with convergence rate O (1/n) N Flammarion, F Bach Conference on Learning Theory, 831--875, 2017 | 26 | 2017 |
| On the effectiveness of adversarial training against common corruptions K Kireev, M Andriushchenko, N Flammarion arXiv preprint arXiv:2103.02325, 2021 | 25 | 2021 |
| Optimal robust linear regression in nearly linear time Y Cherapanamjeri, E Aras, N Tripuraneni, MI Jordan, N Flammarion, ... arXiv preprint arXiv:2007.08137, 2020 | 22 | 2020 |
| Robust discriminative clustering with sparse regularizers N Flammarion, B Palaniappan, F Bach The Journal of Machine Learning Research 18 (1), 2764-2813, 2017 | 20 | 2017 |
| 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 | 18 | 2021 |
| An efficient sampling algorithm for non-smooth composite potentials W Mou, N Flammarion, MJ Wainwright, PL Bartlett arXiv preprint arXiv:1910.00551, 2019 | 15 | 2019 |
Francis BachInria - Ecole Normale SupérieureVerified email at inria.fr
