Nicolas Flammarion

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Maksym AndriushchenkoPostdoctoral Researcher at EPFLDirección de correo verificada de epfl.ch
Francesco CroceEPFLDirección de correo verificada de cs.uni-saarland.de
Matthias HeinProfessor of Computer Science, University of TübingenDirección de correo verificada de uni-tuebingen.de
Francis BachInria - Ecole Normale SupérieureDirección de correo verificada de inria.fr
Peter BartlettProfessor, EECS and Statistics, UC BerkeleyDirección de correo verificada de cs.berkeley.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyDirección de correo verificada de cs.berkeley.edu
Scott PesmePost-Doc at Inria GrenobleDirección de correo verificada de inria.fr
Loucas Pillaud-VivienEcole Nationale Des PontsDirección de correo verificada de enpc.fr
Aditya VarrePhD student, EPFLDirección de correo verificada de epfl.ch
Etienne BoursierINRIA SaclayDirección de correo verificada de inria.fr
Yi-An MaUC San DiegoDirección de correo verificada de ucsd.edu
Mathieu EvenEcole Normale Supérieure, InriaDirección de correo verificada de ens.fr
Niladri S. ChatterjiPostdoctoral Researcher, Department of Computer Science, Stanford UniversityDirección de correo verificada de cs.stanford.edu
Aymeric DieuleveutProfessor, Ecole Polytechnique, FranceDirección de correo verificada de polytechnique.edu
Yeshwanth CherapanamjeriPostdoctoral Associate, MITDirección de correo verificada de berkeley.edu
Yuansi ChenSeminar for Statistics, D-MATH, ETH ZurichDirección de correo verificada de stat.math.ethz.ch
Chi JinAssistant Professor, Princeton UniversityDirección de correo verificada de princeton.edu
Adrien TaylorInria - ENS ParisDirección de correo verificada de inria.fr
Xiang ChengMassachusetts Institute of TechnologyDirección de correo verificada de berkeley.edu
Maryam FazelMoorthy Family Professor of Electrical and Computer Engineering, University of WashingtonDirección de correo verificada de uw.edu

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Nicolas Flammarion

EPFL

Dirección de correo verificada de epfl.ch - Página principal

Machine Learning Optimization Statistics


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Square attack: a query-efficient black-box adversarial attack via random search M Andriushchenko, F Croce, N Flammarion, M Hein ECCV, 2020	1129	2020
Robustbench: a standardized adversarial robustness benchmark F Croce, M Andriushchenko, V Sehwag, E Debenedetti, N Flammarion, ... arXiv preprint arXiv:2010.09670, 2020	772	2020
Understanding and Improving Fast Adversarial Training M Andriushchenko, N Flammarion Advances in Neural Information Processing Systems, 2020	357	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	235	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	181	2017
Is there an analog of Nesterov acceleration for gradient-based MCMC? YA Ma, NS Chatterji, X Cheng, N Flammarion, PL Bartlett, MI Jordan	175	2021
From averaging to acceleration, there is only a step-size N Flammarion, F Bach Conference on learning theory, 658-695, 2015	158	2015
Towards understanding sharpness-aware minimization M Andriushchenko, N Flammarion International Conference on Machine Learning, 639-668, 2022	157	2022
Averaging stochastic gradient descent on Riemannian manifolds N Tripuraneni, N Flammarion, F Bach, MI Jordan Conference On Learning Theory, 2018	120	2018
On the effectiveness of adversarial training against common corruptions K Kireev, M Andriushchenko, N Flammarion Uncertainty in Artificial Intelligence, 1012-1021, 2022	118	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	118	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	114	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	104	2018
Fast mean estimation with sub-Gaussian rates Y Cherapanamjeri, N Flammarion, PL Bartlett Conference on Learning Theory, 786--806, 2019	94	2019
Jailbreaking leading safety-aligned llms with simple adaptive attacks M Andriushchenko, F Croce, N Flammarion arXiv preprint arXiv:2404.02151, 2024	88	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	88	2024
Escaping from saddle points on Riemannian manifolds Y Sun, N Flammarion, M Fazel Advances in Neural Information Processing Systems, 7276-7286, 2019	83	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	79	2022
Optimal rates of statistical seriation N Flammarion, C Mao, P Rigollet	75	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	65	2022

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