| From averaging to acceleration, there is only a step-size N Flammarion, F Bach Conference on Learning Theory, 658-695, 2015 | 112 | 2015 |
| Square attack: a query-efficient black-box adversarial attack via random search M Andriushchenko, F Croce, N Flammarion, M Hein ECCV, 2020 | 108 | 2020 |
| 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 | 101 | 2017 |
| 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 | 75 | 2018 |
| 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 | 65 | 2019 |
| 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 | 56* | 2021 |
| Averaging stochastic gradient descent on Riemannian manifolds N Tripuraneni, N Flammarion, F Bach, MI Jordan Conference On Learning Theory, 2018 | 52 | 2018 |
| Optimal rates of statistical seriation N Flammarion, C Mao, P Rigollet Bernoulli 25 (1), 623-653, 2019 | 47 | 2019 |
| Fast mean estimation with sub-Gaussian rates Y Cherapanamjeri, N Flammarion, PL Bartlett Conference on Learning Theory, 786--806, 2019 | 32 | 2019 |
| Robustbench: a standardized adversarial robustness benchmark F Croce, M Andriushchenko, V Sehwag, N Flammarion, M Chiang, ... arXiv preprint arXiv:2010.09670, 2020 | 29 | 2020 |
| Understanding and Improving Fast Adversarial Training M Andriushchenko, N Flammarion Advances in Neural Information Processing Systems, 2020 | 29 | 2020 |
| Escaping from saddle points on Riemannian manifolds Y Sun, N Flammarion, M Fazel Advances in Neural Information Processing Systems, 7276-7286, 2019 | 26 | 2019 |
| Stochastic Composite Least-Squares Regression with convergence rate O (1/n) N Flammarion, F Bach Conference on Learning Theory, 831--875, 2017 | 20 | 2017 |
| Improved bounds for discretization of Langevin diffusions: Near-optimal rates without convexity W Mou, N Flammarion, MJ Wainwright, PL Bartlett arXiv preprint arXiv:1907.11331, 2019 | 17 | 2019 |
| Robust discriminative clustering with sparse regularizers N Flammarion, B Palaniappan, F Bach The Journal of Machine Learning Research 18 (1), 2764-2813, 2017 | 15 | 2017 |
| An efficient sampling algorithm for non-smooth composite potentials W Mou, N Flammarion, MJ Wainwright, PL Bartlett arXiv preprint arXiv:1910.00551, 2019 | 11 | 2019 |
| Gen-oja: a simple and efficient algorithm for streaming generalized eigenvector computation K Bhatia, A Pacchiano, N Flammarion, PL Bartlett, MI Jordan arXiv preprint arXiv:1811.08393, 2018 | 11 | 2018 |
| Sparse-rs: a versatile framework for query-efficient sparse black-box adversarial attacks F Croce, M Andriushchenko, ND Singh, N Flammarion, M Hein arXiv preprint arXiv:2006.12834, 2020 | 10 | 2020 |
| Optimal robust linear regression in nearly linear time Y Cherapanamjeri, E Aras, N Tripuraneni, MI Jordan, N Flammarion, ... arXiv preprint arXiv:2007.08137, 2020 | 9 | 2020 |
| On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent S Pesme, A Dieuleveut, N Flammarion International Conference on Machine Learning, 2020 | 4 | 2020 |
Francis BachInria - Ecole Normale SupérieureVerified email at inria.fr
