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Nicolas Flammarion
Nicolas Flammarion
EPFL
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Square attack: a query-efficient black-box adversarial attack via random search
M Andriushchenko, F Croce, N Flammarion, M Hein
ECCV, 2020
11422020
Robustbench: a standardized adversarial robustness benchmark
F Croce, M Andriushchenko, V Sehwag, E Debenedetti, N Flammarion, ...
arXiv preprint arXiv:2010.09670, 2020
7792020
Understanding and Improving Fast Adversarial Training
M Andriushchenko, N Flammarion
Advances in Neural Information Processing Systems, 2020
3602020
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
2352019
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
1812017
Is there an analog of Nesterov acceleration for gradient-based MCMC?
YA Ma, NS Chatterji, X Cheng, N Flammarion, PL Bartlett, MI Jordan
1772021
Towards understanding sharpness-aware minimization
M Andriushchenko, N Flammarion
International Conference on Machine Learning, 639-668, 2022
1582022
From averaging to acceleration, there is only a step-size
N Flammarion, F Bach
Conference on learning theory, 658-695, 2015
1582015
Averaging stochastic gradient descent on Riemannian manifolds
N Tripuraneni, N Flammarion, F Bach, MI Jordan
Conference On Learning Theory, 2018
1232018
On the effectiveness of adversarial training against common corruptions
K Kireev, M Andriushchenko, N Flammarion
Uncertainty in Artificial Intelligence, 1012-1021, 2022
1192022
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
1182021
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
1152022
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
1072018
Jailbreaking leading safety-aligned llms with simple adaptive attacks
M Andriushchenko, F Croce, N Flammarion
arXiv preprint arXiv:2404.02151, 2024
942024
Fast mean estimation with sub-Gaussian rates
Y Cherapanamjeri, N Flammarion, PL Bartlett
Conference on Learning Theory, 786--806, 2019
942019
Jailbreakbench: An open robustness benchmark for jailbreaking large language models
P Chao, E Debenedetti, A Robey, M Andriushchenko, F Croce, V Sehwag, ...
arXiv preprint arXiv:2404.01318, 2024
932024
Escaping from saddle points on Riemannian manifolds
Y Sun, N Flammarion, M Fazel
Advances in Neural Information Processing Systems, 7276-7286, 2019
852019
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
802022
Optimal rates of statistical seriation
N Flammarion, C Mao, P Rigollet
752019
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
652022
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