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ORCIDNicolas Flammarion
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2020 – today
- 2024
[c37]Hristo Papazov, Scott Pesme, Nicolas Flammarion:
Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks. AISTATS 2024: 3556-3564- [c36]Oguz Kaan Yüksel, Etienne Boursier, Nicolas Flammarion:
First-order ANIL provably learns representations despite overparametrisation. ICLR 2024 - [c35]Hao Zhao, Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion:
Long Is More for Alignment: A Simple but Tough-to-Beat Baseline for Instruction Fine-Tuning. ICML 2024 - [i53]Etienne Boursier, Nicolas Flammarion:
Early alignment in two-layer networks training is a two-edged sword. CoRR abs/2401.10791 (2024) - [i52]Hao Zhao, Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion:
Long Is More for Alignment: A Simple but Tough-to-Beat Baseline for Instruction Fine-Tuning. CoRR abs/2402.04833 (2024) - [i51]Hristo Papazov, Scott Pesme, Nicolas Flammarion:
Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks. CoRR abs/2403.05293 (2024) - [i50]Patrick Chao, Edoardo Debenedetti, Alexander Robey, Maksym Andriushchenko, Francesco Croce, Vikash Sehwag, Edgar Dobriban, Nicolas Flammarion, George J. Pappas, Florian Tramèr, Hamed Hassani, Eric Wong
:
JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models. CoRR abs/2404.01318 (2024) - [i49]Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion:
Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks. CoRR abs/2404.02151 (2024) - [i48]Javier Rando, Francesco Croce, Krystof Mitka, Stepan Shabalin, Maksym Andriushchenko, Nicolas Flammarion, Florian Tramèr:
Competition Report: Finding Universal Jailbreak Backdoors in Aligned LLMs. CoRR abs/2404.14461 (2024) - [i47]Hao Zhao, Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion:
Is In-Context Learning Sufficient for Instruction Following in LLMs? CoRR abs/2405.19874 (2024) - [i46]Scott Pesme, Radu-Alexandru Dragomir, Nicolas Flammarion:
Implicit Bias of Mirror Flow on Separable Data. CoRR abs/2406.12763 (2024) - [i45]Maksym Andriushchenko, Nicolas Flammarion:
Does Refusal Training in LLMs Generalize to the Past Tense? CoRR abs/2407.11969 (2024) - [i44]Beatriz Borges, Negar Foroutan, Deniz Bayazit, Anna Sotnikova, Syrielle Montariol, Tanya Nazaretzky, Mohammadreza Banaei, Alireza Sakhaeirad, Philippe Servant, Seyed Parsa Neshaei, Jibril Frej, Angelika Romanou, Gail Weiss, Sepideh Mamooler, Zeming Chen, Simin Fan, Silin Gao, Mete Ismayilzada, Debjit Paul, Alexandre Schöpfer, Andrej Janchevski, Anja Tiede, Clarence Linden, Emanuele Troiani, Francesco Salvi, Freya Behrens, Giacomo Orsi, Giovanni Piccioli, Hadrien Sevel, Louis Coulon, Manuela Pineros-Rodriguez, Marin Bonnassies, Pierre Hellich, Puck van Gerwen, Sankalp Gambhir, Solal Pirelli, Thomas Blanchard, Timothée Callens, Toni Abi Aoun, Yannick Calvino Alonso, Yuri Cho, Alberto Silvio Chiappa, Antonio Sclocchi, Étienne Bruno, Florian Hofhammer, Gabriel Pescia, Geovani Rizk, Leello Dadi, Lucas Stoffl, Manoel Horta Ribeiro, Matthieu Bovel, Yueyang Pan, Aleksandra Radenovic, Alexandre Alahi, Alexander Mathis, Anne-Florence Bitbol, Boi Faltings, Cécile Hébert, Devis Tuia, François Maréchal, George Candea, Giuseppe Carleo, Jean-Cédric Chappelier, Nicolas Flammarion, Jean-Marie Fürbringer, Jean-Philippe Pellet, Karl Aberer, Lenka Zdeborová, Marcel Salathé, Martin Jaggi, Martin Rajman, Mathias Payer, Matthieu Wyart, Michael Gastpar, Michele Ceriotti, Ola Svensson, Olivier Lévêque, Paolo Ienne, Rachid Guerraoui, Robert West, Sanidhya Kashyap, Valerio Piazza, Viesturs Simanis, Viktor Kuncak, Volkan Cevher, Philippe Schwaller, Sacha Friedli, Patrick Jermann, Tanja Käser, Antoine Bosselut:
Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants. CoRR abs/2408.11841 (2024) - [i43]Etienne Boursier, Nicolas Flammarion:
Simplicity bias and optimization threshold in two-layer ReLU networks. CoRR abs/2410.02348 (2024) - [i42]Oguz Kaan Yüksel, Mathieu Even, Nicolas Flammarion:
Long-Context Linear System Identification. CoRR abs/2410.05690 (2024) - 2023
- [j4]Omar Fawzi, Nicolas Flammarion, Aurélien Garivier, Aadil Oufkir:
On Adaptivity in Quantum Testing. Trans. Mach. Learn. Res. 2023 (2023) - [c34]Omar Fawzi, Nicolas Flammarion, Aurélien Garivier, Aadil Oufkir:
Quantum Channel Certification with Incoherent Measurements. COLT 2023: 1822-1884 - [c33]Maria-Luiza Vladarean, Nikita Doikov, Martin Jaggi, Nicolas Flammarion:
Linearization Algorithms for Fully Composite Optimization. COLT 2023: 3669-3695 - [c32]Maksym Andriushchenko, Francesco Croce, Maximilian Müller, Matthias Hein, Nicolas Flammarion:
A Modern Look at the Relationship between Sharpness and Generalization. ICML 2023: 840-902 - [c31]Maksym Andriushchenko, Aditya Vardhan Varre, Loucas Pillaud-Vivien, Nicolas Flammarion:
SGD with Large Step Sizes Learns Sparse Features. ICML 2023: 903-925 - [c30]Maksym Andriushchenko, Dara Bahri, Hossein Mobahi, Nicolas Flammarion:
Sharpness-Aware Minimization Leads to Low-Rank Features. NeurIPS 2023 - [c29]Etienne Boursier, Nicolas Flammarion:
Penalising the biases in norm regularisation enforces sparsity. NeurIPS 2023 - [c28]Mathieu Even, Scott Pesme, Suriya Gunasekar, Nicolas Flammarion:
(S)GD over Diagonal Linear Networks: Implicit bias, Large Stepsizes and Edge of Stability. NeurIPS 2023 - [c27]Klim Kireev, Maksym Andriushchenko, Carmela Troncoso, Nicolas Flammarion:
Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings. NeurIPS 2023 - [c26]Scott Pesme, Nicolas Flammarion:
Saddle-to-Saddle Dynamics in Diagonal Linear Networks. NeurIPS 2023 - [c25]Aditya Vardhan Varre, Maria-Luiza Vladarean, Loucas Pillaud-Vivien, Nicolas Flammarion:
On the spectral bias of two-layer linear networks. NeurIPS 2023 - [i41]Maksym Andriushchenko, Francesco Croce, Maximilian Müller, Matthias Hein, Nicolas Flammarion:
A modern look at the relationship between sharpness and generalization. CoRR abs/2302.07011 (2023) - [i40]Mathieu Even, Scott Pesme, Suriya Gunasekar, Nicolas Flammarion:
(S)GD over Diagonal Linear Networks: Implicit Regularisation, Large Stepsizes and Edge of Stability. CoRR abs/2302.08982 (2023) - [i39]Maria-Luiza Vladarean, Nikita Doikov, Martin Jaggi, Nicolas Flammarion:
Linearization Algorithms for Fully Composite Optimization. CoRR abs/2302.12808 (2023) - [i38]Omar Fawzi, Nicolas Flammarion, Aurélien Garivier, Aadil Oufkir:
Quantum Channel Certification with Incoherent Strategies. CoRR abs/2303.01188 (2023) - [i37]Yüksel Oguz, Etienne Boursier, Nicolas Flammarion:
Model agnostic methods meta-learn despite misspecifications. CoRR abs/2303.01335 (2023) - [i36]Etienne Boursier, Nicolas Flammarion:
Penalising the biases in norm regularisation enforces sparsity. CoRR abs/2303.01353 (2023) - [i35]Scott Pesme, Nicolas Flammarion:
Saddle-to-Saddle Dynamics in Diagonal Linear Networks. CoRR abs/2304.00488 (2023) - [i34]Maksym Andriushchenko, Dara Bahri, Hossein Mobahi, Nicolas Flammarion:
Sharpness-Aware Minimization Leads to Low-Rank Features. CoRR abs/2305.16292 (2023) - [i33]Klim Kireev, Maksym Andriushchenko, Carmela Troncoso, Nicolas Flammarion:
Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings. CoRR abs/2306.04064 (2023) - [i32]Maksym Andriushchenko, Francesco D'Angelo, Aditya Varre, Nicolas Flammarion:
Why Do We Need Weight Decay in Modern Deep Learning? CoRR abs/2310.04415 (2023) - 2022
- [j3]Wenlong Mou, Nicolas Flammarion, Martin J. Wainwright, Peter L. Bartlett:
An Efficient Sampling Algorithm for Non-smooth Composite Potentials. J. Mach. Learn. Res. 23: 233:1-233:50 (2022) - [c24]Francesco Croce, Maksym Andriushchenko, Naman D. Singh, Nicolas Flammarion, Matthias Hein:
Sparse-RS: A Versatile Framework for Query-Efficient Sparse Black-Box Adversarial Attacks. AAAI 2022: 6437-6445 - [c23]Etienne Boursier, Mikhail Konobeev, Nicolas Flammarion:
Trace norm regularization for multi-task learning with scarce data. COLT 2022: 1303-1327 - [c22]Aditya Varre, Nicolas Flammarion:
Accelerated SGD for Non-Strongly-Convex Least Squares. COLT 2022: 2062-2126 - [c21]Loucas Pillaud-Vivien, Julien Reygner, Nicolas Flammarion:
Label noise (stochastic) gradient descent implicitly solves the Lasso for quadratic parametrisation. COLT 2022: 2127-2159 - [c20]Maksym Andriushchenko, Xiaoyang Rebecca Li, Geoffrey Oxholm, Thomas Gittings, Tu Bui, Nicolas Flammarion, John P. Collomosse:
ARIA: Adversarially Robust Image Attribution for Content Provenance. CVPR Workshops 2022: 33-43 - [c19]Maksym Andriushchenko, Nicolas Flammarion:
Towards Understanding Sharpness-Aware Minimization. ICML 2022: 639-668 - [c18]Etienne Boursier, Loucas Pillaud-Vivien, Nicolas Flammarion:
Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs. NeurIPS 2022 - [c17]Klim Kireev, Maksym Andriushchenko, Nicolas Flammarion:
On the effectiveness of adversarial training against common corruptions. UAI 2022: 1012-1021 - [i31]Etienne Boursier, Mikhail Konobeev, Nicolas Flammarion:
Trace norm regularization for multi-task learning with scarce data. CoRR abs/2202.06742 (2022) - [i30]Maksym Andriushchenko, Xiaoyang Rebecca Li, Geoffrey Oxholm, Thomas Gittings, Tu Bui, Nicolas Flammarion, John P. Collomosse:
ARIA: Adversarially Robust Image Attribution for Content Provenance. CoRR abs/2202.12860 (2022) - [i29]Aditya Varre, Nicolas Flammarion:
Accelerated SGD for Non-Strongly-Convex Least Squares. CoRR abs/2203.01744 (2022) - [i28]Omar Fawzi, Nicolas Flammarion, Aurélien Garivier, Aadil Oufkir:
Sequential algorithms for testing identity and closeness of distributions. CoRR abs/2205.06069 (2022) - [i27]Etienne Boursier, Loucas Pillaud-Vivien, Nicolas Flammarion:
Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs. CoRR abs/2206.00939 (2022) - [i26]Maksym Andriushchenko, Nicolas Flammarion:
Towards Understanding Sharpness-Aware Minimization. CoRR abs/2206.06232 (2022) - [i25]Loucas Pillaud-Vivien, Julien Reygner, Nicolas Flammarion:
Label noise (stochastic) gradient descent implicitly solves the Lasso for quadratic parametrisation. CoRR abs/2206.09841 (2022) - [i24]Maksym Andriushchenko, Aditya Varre, Loucas Pillaud-Vivien, Nicolas Flammarion:
SGD with large step sizes learns sparse features. CoRR abs/2210.05337 (2022) - 2021
- [c16]Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Edoardo Debenedetti, Nicolas Flammarion, Mung Chiang, Prateek Mittal, Matthias Hein:
RobustBench: a standardized adversarial robustness benchmark. NeurIPS Datasets and Benchmarks 2021 - [c15]Aadil Oufkir, Omar Fawzi, Nicolas Flammarion, Aurélien Garivier:
Sequential Algorithms for Testing Closeness of Distributions. NeurIPS 2021: 11655-11664 - [c14]Aditya Vardhan Varre, Loucas Pillaud-Vivien, Nicolas Flammarion:
Last iterate convergence of SGD for Least-Squares in the Interpolation regime. NeurIPS 2021: 21581-21591 - [c13]Mathieu Even, Raphaël Berthier, Francis R. Bach, Nicolas Flammarion, Hadrien Hendrikx, Pierre Gaillard, Laurent Massoulié, Adrien B. Taylor:
Continuized Accelerations of Deterministic and Stochastic Gradient Descents, and of Gossip Algorithms. NeurIPS 2021: 28054-28066 - [c12]Scott Pesme, Loucas Pillaud-Vivien, Nicolas Flammarion:
Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity. NeurIPS 2021: 29218-29230 - [i23]Aditya Varre, Loucas Pillaud-Vivien, Nicolas Flammarion:
Last iterate convergence of SGD for Least-Squares in the Interpolation regime. CoRR abs/2102.03183 (2021) - [i22]Raphaël Berthier, Francis R. Bach, Nicolas Flammarion, Pierre Gaillard, Adrien B. Taylor:
A Continuized View on Nesterov Acceleration. CoRR abs/2102.06035 (2021) - [i21]Klim Kireev, Maksym Andriushchenko, Nicolas Flammarion:
On the effectiveness of adversarial training against common corruptions. CoRR abs/2103.02325 (2021) - [i20]Mathieu Even, Raphaël Berthier, Francis R. Bach, Nicolas Flammarion, Pierre Gaillard, Hadrien Hendrikx, Laurent Massoulié, Adrien B. Taylor:
A Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip. CoRR abs/2106.07644 (2021) - [i19]Scott Pesme, Loucas Pillaud-Vivien, Nicolas Flammarion:
Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity. CoRR abs/2106.09524 (2021) - [i18]El Mahdi Chayti, Sai Praneeth Karimireddy, Sebastian U. Stich, Nicolas Flammarion, Martin Jaggi:
Linear Speedup in Personalized Collaborative Learning. CoRR abs/2111.05968 (2021) - 2020
- [c11]Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion, Matthias Hein:
Square Attack: A Query-Efficient Black-Box Adversarial Attack via Random Search. ECCV (23) 2020: 484-501 - [c10]Scott Pesme, Aymeric Dieuleveut, Nicolas Flammarion:
On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent. ICML 2020: 7641-7651 - [c9]Maksym Andriushchenko, Nicolas Flammarion:
Understanding and Improving Fast Adversarial Training. NeurIPS 2020 - [c8]Scott Pesme, Nicolas Flammarion:
Online Robust Regression via SGD on the l1 loss. NeurIPS 2020 - [i17]Francesco Croce, Maksym Andriushchenko, Naman D. Singh, Nicolas Flammarion, Matthias Hein:
Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks. CoRR abs/2006.12834 (2020) - [i16]Scott Pesme, Nicolas Flammarion:
Online Robust Regression via SGD on the l1 loss. CoRR abs/2007.00399 (2020) - [i15]Scott Pesme, Aymeric Dieuleveut, Nicolas Flammarion:
On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent. CoRR abs/2007.00534 (2020) - [i14]Maksym Andriushchenko, Nicolas Flammarion:
Understanding and Improving Fast Adversarial Training. CoRR abs/2007.02617 (2020) - [i13]Yeshwanth Cherapanamjeri, Efe Aras, Nilesh Tripuraneni, Michael I. Jordan, Nicolas Flammarion, Peter L. Bartlett:
Optimal Robust Linear Regression in Nearly Linear Time. CoRR abs/2007.08137 (2020) - [i12]Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Nicolas Flammarion, Mung Chiang, Prateek Mittal, Matthias Hein:
RobustBench: a standardized adversarial robustness benchmark. CoRR abs/2010.09670 (2020)
2010 – 2019
- 2019
- [c7]Yeshwanth Cherapanamjeri, Nicolas Flammarion, Peter L. Bartlett:
Fast Mean Estimation with Sub-Gaussian Rates. COLT 2019: 786-806 - [c6]Yue Sun, Nicolas Flammarion, Maryam Fazel:
Escaping from saddle points on Riemannian manifolds. NeurIPS 2019: 7274-7284 - [i11]Yi-An Ma, Niladri S. Chatterji, Xiang Cheng, Nicolas Flammarion, Peter L. Bartlett, Michael I. Jordan:
Is There an Analog of Nesterov Acceleration for MCMC? CoRR abs/1902.00996 (2019) - [i10]Yeshwanth Cherapanamjeri, Nicolas Flammarion, Peter L. Bartlett:
Fast Mean Estimation with Sub-Gaussian Rates. CoRR abs/1902.01998 (2019) - [i9]Yue Sun, Nicolas Flammarion, Maryam Fazel:
Escaping from saddle points on Riemannian manifolds. CoRR abs/1906.07355 (2019) - [i8]Wenlong Mou, Nicolas Flammarion, Martin J. Wainwright, Peter L. Bartlett:
An Efficient Sampling Algorithm for Non-smooth Composite Potentials. CoRR abs/1910.00551 (2019) - [i7]Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion, Matthias Hein:
Square Attack: a query-efficient black-box adversarial attack via random search. CoRR abs/1912.00049 (2019) - 2018
- [c5]Nilesh Tripuraneni, Nicolas Flammarion, Francis R. Bach, Michael I. Jordan:
Averaging Stochastic Gradient Descent on Riemannian Manifolds. COLT 2018: 650-687 - [c4]Niladri S. Chatterji, Nicolas Flammarion, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan:
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo. ICML 2018: 763-772 - [c3]Kush Bhatia, Aldo Pacchiano, Nicolas Flammarion, Peter L. Bartlett, Michael I. Jordan:
Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation. NeurIPS 2018: 7016-7025 - [i6]Niladri S. Chatterji, Nicolas Flammarion, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan:
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo. CoRR abs/1802.05431 (2018) - [i5]Nilesh Tripuraneni, Nicolas Flammarion, Francis R. Bach, Michael I. Jordan:
Averaging Stochastic Gradient Descent on Riemannian Manifolds. CoRR abs/1802.09128 (2018) - [i4]Kush Bhatia, Aldo Pacchiano, Nicolas Flammarion, Peter L. Bartlett, Michael I. Jordan:
Gen-Oja: A Simple and Efficient Algorithm for Streaming Generalized Eigenvector Computation. CoRR abs/1811.08393 (2018) - [i3]Yi-An Ma, Yuansi Chen, Chi Jin, Nicolas Flammarion, Michael I. Jordan:
Sampling Can Be Faster Than Optimization. CoRR abs/1811.08413 (2018) - 2017
- [b1]Nicolas Flammarion:
Stochastic Approximation and Least-Squares Regression, with Applications to Machine Learning. (Approximation Stochastique et Régression par Moindres Carrés : Applications en Apprentissage Automatique). École Normale Supérieure, Paris, France, 2017 - [j2]Nicolas Flammarion, Balamurugan Palaniappan, Francis R. Bach:
Robust Discriminative Clustering with Sparse Regularizers. J. Mach. Learn. Res. 18: 80:1-80:50 (2017) - [j1]Aymeric Dieuleveut, Nicolas Flammarion, Francis R. Bach:
Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression. J. Mach. Learn. Res. 18: 101:1-101:51 (2017) - [c2]Nicolas Flammarion, Francis R. Bach:
Stochastic Composite Least-Squares Regression with Convergence Rate $O(1/n)$. COLT 2017: 831-875 - 2016
- [i2]Aymeric Dieuleveut, Nicolas Flammarion, Francis R. Bach:
Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression. CoRR abs/1602.05419 (2016) - [i1]Nicolas Flammarion, Balamurugan Palaniappan, Francis R. Bach:
Robust Discriminative Clustering with Sparse Regularizers. CoRR abs/1608.08052 (2016) - 2015
- [c1]Nicolas Flammarion, Francis R. Bach:
From Averaging to Acceleration, There is Only a Step-size. COLT 2015: 658-695
Coauthor Index
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