Nicolas Flammarion

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Maksym AndriushchenkoPhD student at EPFLAdresse e-mail validée de epfl.ch
Francesco CroceEPFLAdresse e-mail validée de cs.uni-saarland.de
Matthias HeinProfessor of Computer Science, University of TübingenAdresse e-mail validée de uni-tuebingen.de
Francis BachInria - Ecole Normale SupérieureAdresse e-mail validée de inria.fr
Peter BartlettProfessor, EECS and Statistics, UC BerkeleyAdresse e-mail validée de cs.berkeley.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyAdresse e-mail validée de cs.berkeley.edu
Scott PesmePhD at EPFLAdresse e-mail validée de epfl.ch
Loucas Pillaud-VivienCourant Institute of Mathematics, NYU / Flatiron Institute, New YorkAdresse e-mail validée de flatironinstitute.org
Yi-An MaUC San DiegoAdresse e-mail validée de ucsd.edu
Aditya VarrePhD student, EPFLAdresse e-mail validée de epfl.ch
Etienne BoursierINRIA SaclayAdresse e-mail validée de inria.fr
Niladri S. ChatterjiPostdoctoral Researcher, Department of Computer Science, Stanford UniversityAdresse e-mail validée de cs.stanford.edu
Aymeric DieuleveutProfessor, Ecole Polytechnique, FranceAdresse e-mail validée de polytechnique.edu
Mathieu EvenEcole Normale Supérieure, InriaAdresse e-mail validée de ens.fr
Yeshwanth CherapanamjeriPh.D Student, UC BerkeleyAdresse e-mail validée de berkeley.edu
Yuansi ChenSeminar for Statistics, D-MATH, ETH ZurichAdresse e-mail validée de stat.math.ethz.ch
Chi JinAssistant Professor, Princeton UniversityAdresse e-mail validée de princeton.edu
Xiang ChengMassachusetts Institute of TechnologyAdresse e-mail validée de berkeley.edu
Adrien TaylorInria - ENS ParisAdresse e-mail validée de inria.fr
Maryam FazelMoorthy Family Professor of Electrical and Computer Engineering, University of WashingtonAdresse e-mail validée de uw.edu

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Nicolas Flammarion

EPFL

Adresse e-mail validée de epfl.ch - Page d'accueil

Machine Learning Optimization Statistics


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Square attack: a query-efficient black-box adversarial attack via random search M Andriushchenko, F Croce, N Flammarion, M Hein ECCV, 2020	1018	2020
Robustbench: a standardized adversarial robustness benchmark F Croce, M Andriushchenko, V Sehwag, E Debenedetti, N Flammarion, ... arXiv preprint arXiv:2010.09670, 2020	683	2020
Understanding and Improving Fast Adversarial Training M Andriushchenko, N Flammarion Advances in Neural Information Processing Systems, 2020	323	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	215	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	172	2017
Is there an analog of Nesterov acceleration for gradient-based MCMC? YA Ma, NS Chatterji, X Cheng, N Flammarion, PL Bartlett, MI Jordan	161	2021
From averaging to acceleration, there is only a step-size N Flammarion, F Bach Conference on learning theory, 658-695, 2015	155	2015
Towards understanding sharpness-aware minimization M Andriushchenko, N Flammarion International Conference on Machine Learning, 639-668, 2022	128	2022
Averaging stochastic gradient descent on Riemannian manifolds N Tripuraneni, N Flammarion, F Bach, MI Jordan Conference On Learning Theory, 2018	117	2018
On the effectiveness of adversarial training against common corruptions K Kireev, M Andriushchenko, N Flammarion Uncertainty in Artificial Intelligence, 1012-1021, 2022	101	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	101	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	101	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	100	2018
Fast mean estimation with sub-Gaussian rates Y Cherapanamjeri, N Flammarion, PL Bartlett Conference on Learning Theory, 786--806, 2019	88	2019
Escaping from saddle points on Riemannian manifolds Y Sun, N Flammarion, M Fazel Advances in Neural Information Processing Systems, 7276-7286, 2019	82	2019
Optimal rates of statistical seriation N Flammarion, C Mao, P Rigollet	73	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	71	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	56	2022
Sgd with large step sizes learns sparse features M Andriushchenko, AV Varre, L Pillaud-Vivien, N Flammarion International Conference on Machine Learning, 903-925, 2023	51	2023
A modern look at the relationship between sharpness and generalization M Andriushchenko, F Croce, M Müller, M Hein, N Flammarion International Conference on Machine Learning 202, 840--902, 2023	43	2023

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