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Nicolas 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 - [i50]Etienne Boursier, Nicolas Flammarion:
Early alignment in two-layer networks training is a two-edged sword. CoRR abs/2401.10791 (2024) - [i49]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) - [i48]Hristo Papazov, Scott Pesme, Nicolas Flammarion:
Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks. CoRR abs/2403.05293 (2024) - [i47]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) - [i46]Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion:
Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks. CoRR abs/2404.02151 (2024) - [i45]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) - [i44]Hao Zhao, Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion:
Is In-Context Learning Sufficient for Instruction Following in LLMs? CoRR abs/2405.19874 (2024) - [i43]Scott Pesme, Radu-Alexandru Dragomir, Nicolas Flammarion:
Implicit Bias of Mirror Flow on Separable Data. CoRR abs/2406.12763 (2024) - [i42]Maksym Andriushchenko, Nicolas Flammarion:
Does Refusal Training in LLMs Generalize to the Past Tense? CoRR abs/2407.11969 (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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