Nicolas Flammarion

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 89

Top Venues

CoRR

NeurIPS

COLT

ICML

École Normale Supérieure, Paris, France (PHD)

Education History

École Normale Supérieure, Paris, France

PHD

Publications

Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion
Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks. CoRR (2024)
Oguz Kaan Yüksel, Etienne Boursier, Nicolas Flammarion
First-order ANIL provably learns representations despite overparametrisation. ICLR (2024)
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 (2024)
Scott Pesme, Radu-Alexandru Dragomir, Nicolas Flammarion
Implicit Bias of Mirror Flow on Separable Data. CoRR (2024)
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 (2024)
Hristo Papazov, Scott Pesme, Nicolas Flammarion
Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks. CoRR (2024)
Hristo Papazov, Scott Pesme, Nicolas Flammarion
Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks. AISTATS (2024)
Maksym Andriushchenko, Nicolas Flammarion
Does Refusal Training in LLMs Generalize to the Past Tense? CoRR (2024)
Hao Zhao, Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion
Is In-Context Learning Sufficient for Instruction Following in LLMs? CoRR (2024)
Etienne Boursier, Nicolas Flammarion
Early alignment in two-layer networks training is a two-edged sword. CoRR (2024)
Javier Rando, Francesco Croce, Krystof Mitka, Stepan Shabalin, Maksym Andriushchenko, Nicolas Flammarion, Florian Tramèr
Competition Report: Finding Universal Jailbreak Backdoors in Aligned LLMs. CoRR (2024)
Maria-Luiza Vladarean, Nikita Doikov, Martin Jaggi, Nicolas Flammarion
Linearization Algorithms for Fully Composite Optimization. COLT (2023)
Omar Fawzi, Nicolas Flammarion, Aurélien Garivier, Aadil Oufkir
Quantum Channel Certification with Incoherent Strategies. CoRR (2023)
Maksym Andriushchenko, Francesco Croce, Maximilian Müller, Matthias Hein, Nicolas Flammarion
A modern look at the relationship between sharpness and generalization. CoRR (2023)
Maksym Andriushchenko, Francesco Croce, Maximilian Müller, Matthias Hein, Nicolas Flammarion
A Modern Look at the Relationship between Sharpness and Generalization. ICML (2023)
Omar Fawzi, Nicolas Flammarion, Aurélien Garivier, Aadil Oufkir
Quantum Channel Certification with Incoherent Measurements. COLT (2023)
Mathieu Even, Scott Pesme, Suriya Gunasekar, Nicolas Flammarion
(S)GD over Diagonal Linear Networks: Implicit Regularisation, Large Stepsizes and Edge of Stability. CoRR (2023)
Omar Fawzi, Nicolas Flammarion, Aurélien Garivier, Aadil Oufkir
On Adaptivity in Quantum Testing. Trans. Mach. Learn. Res. 2023 (2023)
Maria-Luiza Vladarean, Nikita Doikov, Martin Jaggi, Nicolas Flammarion
Linearization Algorithms for Fully Composite Optimization. CoRR (2023)
Scott Pesme, Nicolas Flammarion
Saddle-to-Saddle Dynamics in Diagonal Linear Networks. CoRR (2023)
Mathieu Even, Scott Pesme, Suriya Gunasekar, Nicolas Flammarion
(S)GD over Diagonal Linear Networks: Implicit bias, Large Stepsizes and Edge of Stability. NeurIPS (2023)
Maksym Andriushchenko, Dara Bahri, Hossein Mobahi, Nicolas Flammarion
Sharpness-Aware Minimization Leads to Low-Rank Features. NeurIPS (2023)
Etienne Boursier, Nicolas Flammarion
Penalising the biases in norm regularisation enforces sparsity. NeurIPS (2023)
Maksym Andriushchenko, Dara Bahri, Hossein Mobahi, Nicolas Flammarion
Sharpness-Aware Minimization Leads to Low-Rank Features. CoRR (2023)
Aditya Vardhan Varre, Maria-Luiza Vladarean, Loucas Pillaud-Vivien, Nicolas Flammarion
On the spectral bias of two-layer linear networks. NeurIPS (2023)
Klim Kireev, Maksym Andriushchenko, Carmela Troncoso, Nicolas Flammarion
Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings. CoRR (2023)
Maksym Andriushchenko, Francesco D'Angelo, Aditya Varre, Nicolas Flammarion
Why Do We Need Weight Decay in Modern Deep Learning? CoRR (2023)
Scott Pesme, Nicolas Flammarion
Saddle-to-Saddle Dynamics in Diagonal Linear Networks. NeurIPS (2023)
Klim Kireev, Maksym Andriushchenko, Carmela Troncoso, Nicolas Flammarion
Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings. NeurIPS (2023)
Etienne Boursier, Nicolas Flammarion
Penalising the biases in norm regularisation enforces sparsity. CoRR (2023)
Maksym Andriushchenko, Aditya Vardhan Varre, Loucas Pillaud-Vivien, Nicolas Flammarion
SGD with Large Step Sizes Learns Sparse Features. ICML (2023)
Yüksel Oguz, Etienne Boursier, Nicolas Flammarion
Model agnostic methods meta-learn despite misspecifications. CoRR (2023)
Loucas Pillaud-Vivien, Julien Reygner, Nicolas Flammarion
Label noise (stochastic) gradient descent implicitly solves the Lasso for quadratic parametrisation. COLT (2022)
Etienne Boursier, Loucas Pillaud-Vivien, Nicolas Flammarion
Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs. CoRR (2022)
Omar Fawzi, Nicolas Flammarion, Aurélien Garivier, Aadil Oufkir
Sequential algorithms for testing identity and closeness of distributions. CoRR (2022)
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)
Maksym Andriushchenko, Aditya Varre, Loucas Pillaud-Vivien, Nicolas Flammarion
SGD with large step sizes learns sparse features. CoRR (2022)
Loucas Pillaud-Vivien, Julien Reygner, Nicolas Flammarion
Label noise (stochastic) gradient descent implicitly solves the Lasso for quadratic parametrisation. CoRR (2022)
Etienne Boursier, Mikhail Konobeev, Nicolas Flammarion
Trace norm regularization for multi-task learning with scarce data. COLT (2022)
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 (2022)
Wenlong Mou, Nicolas Flammarion, Martin J. Wainwright, Peter L. Bartlett
An Efficient Sampling Algorithm for Non-smooth Composite Potentials. J. Mach. Learn. Res. 23 (2022)
Klim Kireev, Maksym Andriushchenko, Nicolas Flammarion
On the effectiveness of adversarial training against common corruptions. UAI (2022)
Maksym Andriushchenko, Nicolas Flammarion
Towards Understanding Sharpness-Aware Minimization. CoRR (2022)
Maksym Andriushchenko, Nicolas Flammarion
Towards Understanding Sharpness-Aware Minimization. ICML (2022)
Etienne Boursier, Mikhail Konobeev, Nicolas Flammarion
Trace norm regularization for multi-task learning with scarce data. CoRR (2022)
Etienne Boursier, Loucas Pillaud-Vivien, Nicolas Flammarion
Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs. NeurIPS (2022)
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)
Aditya Varre, Nicolas Flammarion
Accelerated SGD for Non-Strongly-Convex Least Squares. COLT (2022)
Aditya Varre, Nicolas Flammarion
Accelerated SGD for Non-Strongly-Convex Least Squares. CoRR (2022)
Aditya Vardhan Varre, Loucas Pillaud-Vivien, Nicolas Flammarion
Last iterate convergence of SGD for Least-Squares in the Interpolation regime. NeurIPS (2021)
El Mahdi Chayti, Sai Praneeth Karimireddy, Sebastian U. Stich, Nicolas Flammarion, Martin Jaggi
Linear Speedup in Personalized Collaborative Learning. CoRR (2021)
Scott Pesme, Loucas Pillaud-Vivien, Nicolas Flammarion
Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity. CoRR (2021)
Scott Pesme, Loucas Pillaud-Vivien, Nicolas Flammarion
Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity. NeurIPS (2021)
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)
Aditya Varre, Loucas Pillaud-Vivien, Nicolas Flammarion
Last iterate convergence of SGD for Least-Squares in the Interpolation regime. CoRR (2021)
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)
Mathieu Even, Raphaël Berthier, Francis R. Bach, Nicolas Flammarion, Pierre Gaillard, Hadrien Hendrikx, Laurent Massoulié, Adrien Taylor
A Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip. CoRR (2021)
Klim Kireev, Maksym Andriushchenko, Nicolas Flammarion
On the effectiveness of adversarial training against common corruptions. CoRR (2021)
Aadil Oufkir, Omar Fawzi, Nicolas Flammarion, Aurélien Garivier
Sequential Algorithms for Testing Closeness of Distributions. NeurIPS (2021)
Raphaël Berthier, Francis R. Bach, Nicolas Flammarion, Pierre Gaillard, Adrien Taylor
A Continuized View on Nesterov Acceleration. CoRR (2021)
Scott Pesme, Nicolas Flammarion
Online Robust Regression via SGD on the l1 loss. NeurIPS (2020)
Yeshwanth Cherapanamjeri, Efe Aras, Nilesh Tripuraneni, Michael I. Jordan, Nicolas Flammarion, Peter L. Bartlett
Optimal Robust Linear Regression in Nearly Linear Time. CoRR (2020)
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 (2020)
Scott Pesme, Nicolas Flammarion
Online Robust Regression via SGD on the l1 loss. CoRR (2020)
Scott Pesme, Aymeric Dieuleveut, Nicolas Flammarion
On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent. ICML (2020)
Maksym Andriushchenko, Nicolas Flammarion
Understanding and Improving Fast Adversarial Training. NeurIPS (2020)
Maksym Andriushchenko, Nicolas Flammarion
Understanding and Improving Fast Adversarial Training. CoRR (2020)
Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Nicolas Flammarion, Mung Chiang, Prateek Mittal, Matthias Hein
RobustBench: a standardized adversarial robustness benchmark. CoRR (2020)
Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion, Matthias Hein
Square Attack: A Query-Efficient Black-Box Adversarial Attack via Random Search. ECCV (23) (2020)
Scott Pesme, Aymeric Dieuleveut, Nicolas Flammarion
On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent. CoRR (2020)
Yue Sun, Nicolas Flammarion, Maryam Fazel
Escaping from saddle points on Riemannian manifolds. NeurIPS (2019)
Yue Sun, Nicolas Flammarion, Maryam Fazel
Escaping from saddle points on Riemannian manifolds. CoRR (2019)
Wenlong Mou, Nicolas Flammarion, Martin J. Wainwright, Peter L. Bartlett
An Efficient Sampling Algorithm for Non-smooth Composite Potentials. CoRR (2019)
Yeshwanth Cherapanamjeri, Nicolas Flammarion, Peter L. Bartlett
Fast Mean Estimation with Sub-Gaussian Rates. CoRR (2019)
Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion, Matthias Hein
Square Attack: a query-efficient black-box adversarial attack via random search. CoRR (2019)
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 (2019)
Yeshwanth Cherapanamjeri, Nicolas Flammarion, Peter L. Bartlett
Fast Mean Estimation with Sub-Gaussian Rates. COLT (2019)
Yi-An Ma, Yuansi Chen, Chi Jin, Nicolas Flammarion, Michael I. Jordan
Sampling Can Be Faster Than Optimization. CoRR (2018)
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)
Nilesh Tripuraneni, Nicolas Flammarion, Francis R. Bach, Michael I. Jordan
Averaging Stochastic Gradient Descent on Riemannian Manifolds. CoRR (2018)
Nilesh Tripuraneni, Nicolas Flammarion, Francis R. Bach, Michael I. Jordan
Averaging Stochastic Gradient Descent on Riemannian Manifolds. COLT (2018)
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 (2018)
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 (2018)
Kush Bhatia, Aldo Pacchiano, Nicolas Flammarion, Peter L. Bartlett, Michael I. Jordan
Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation. NeurIPS (2018)
Nicolas Flammarion, Balamurugan Palaniappan, Francis R. Bach
Robust Discriminative Clustering with Sparse Regularizers. J. Mach. Learn. Res. 18 (2017)
Aymeric Dieuleveut, Nicolas Flammarion, Francis R. Bach
Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression. J. Mach. Learn. Res. 18 (2017)
Nicolas Flammarion, Francis R. Bach
Stochastic Composite Least-Squares Regression with Convergence Rate $O(1/n)$. COLT (2017)
Nicolas Flammarion, Balamurugan Palaniappan, Francis R. Bach
Robust Discriminative Clustering with Sparse Regularizers. CoRR (2016)
Aymeric Dieuleveut, Nicolas Flammarion, Francis R. Bach
Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression. CoRR (2016)
Nicolas Flammarion, Francis R. Bach
From Averaging to Acceleration, There is Only a Step-size. COLT (2015)

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Publication Activity (10 Years)

Top Topics

Top Venues

Nicolas Flammarion