Nicolas Flammarion

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Maksym AndriushchenkoPostdoctoral Researcher at EPFLVerified email at epfl.ch
Francesco CroceEPFLVerified email at cs.uni-saarland.de
Matthias HeinProfessor of Computer Science, University of TübingenVerified email at uni-tuebingen.de
Francis BachInria - Ecole Normale SupérieureVerified email at inria.fr
Peter BartlettProfessor, EECS and Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Scott PesmePost-Doc at Inria GrenobleVerified email at inria.fr
Loucas Pillaud-VivienEcole Nationale Des PontsVerified email at enpc.fr
Aditya VarrePhD student, EPFLVerified email at epfl.ch
Etienne BoursierINRIA SaclayVerified email at inria.fr
Yi-An MaUC San DiegoVerified email at ucsd.edu
Mathieu EvenEcole Normale Supérieure, InriaVerified email at ens.fr
Niladri S. ChatterjiPostdoctoral Researcher, Department of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
Aymeric DieuleveutProfessor, Ecole Polytechnique, FranceVerified email at polytechnique.edu
Yeshwanth CherapanamjeriPostdoctoral Associate, MITVerified email at berkeley.edu
Yuansi ChenSeminar for Statistics, D-MATH, ETH ZurichVerified email at stat.math.ethz.ch
Chi JinAssistant Professor, Princeton UniversityVerified email at princeton.edu
Adrien TaylorInria - ENS ParisVerified email at inria.fr
Xiang ChengMassachusetts Institute of TechnologyVerified email at berkeley.edu
Maryam FazelMoorthy Family Professor of Electrical and Computer Engineering, University of WashingtonVerified email at uw.edu

Nicolas Flammarion

EPFL

Verified email at epfl.ch - Homepage

Machine Learning Optimization Statistics


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Square attack: a query-efficient black-box adversarial attack via random search M Andriushchenko, F Croce, N Flammarion, M Hein ECCV, 2020	1188	2020
Robustbench: a standardized adversarial robustness benchmark F Croce, M Andriushchenko, V Sehwag, E Debenedetti, N Flammarion, ... arXiv preprint arXiv:2010.09670, 2020	821	2020
Understanding and Improving Fast Adversarial Training M Andriushchenko, N Flammarion Advances in Neural Information Processing Systems, 2020	365	2020
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	246	2019
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	187	2017
Is there an analog of Nesterov acceleration for gradient-based MCMC? YA Ma, NS Chatterji, X Cheng, N Flammarion, PL Bartlett, MI Jordan	181	2021
Towards understanding sharpness-aware minimization M Andriushchenko, N Flammarion International conference on machine learning, 639-668, 2022	174	2022
From averaging to acceleration, there is only a step-size N Flammarion, F Bach Conference on learning theory, 658-695, 2015	154	2015
Jailbreaking leading safety-aligned llms with simple adaptive attacks M Andriushchenko, F Croce, N Flammarion arXiv preprint arXiv:2404.02151, 2024	141	2024
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	132	2024
Averaging stochastic gradient descent on Riemannian manifolds N Tripuraneni, N Flammarion, F Bach, MI Jordan Conference On Learning Theory, 2018	127	2018
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	123	2021
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	122	2022
On the effectiveness of adversarial training against common corruptions K Kireev, M Andriushchenko, N Flammarion Uncertainty in Artificial Intelligence, 1012-1021, 2022	121	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	109	2018
Fast mean estimation with sub-Gaussian rates Y Cherapanamjeri, N Flammarion, PL Bartlett Conference on Learning Theory, 786--806, 2019	95	2019
Escaping from saddle points on Riemannian manifolds Y Sun, N Flammarion, M Fazel Advances in Neural Information Processing Systems, 7276-7286, 2019	88	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	82	2022
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	75	2022
Optimal rates of statistical seriation N Flammarion, C Mao, P Rigollet	74	2019

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