Nicolas Flammarion

According to our database, Nicolas Flammarion authored at least 54 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks.

Hristo Papazov, Scott Pesme, Nicolas Flammarion

CoRR, 2024

Long Is More for Alignment: A Simple but Tough-to-Beat Baseline for Instruction Fine-Tuning.

Hao Zhao, Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion

CoRR, 2024

Early alignment in two-layer networks training is a two-edged sword.

Etienne Boursier, Nicolas Flammarion

CoRR, 2024

2023

Why Do We Need Weight Decay in Modern Deep Learning?

Maksym Andriushchenko, Francesco D'Angelo, Aditya Varre, Nicolas Flammarion

CoRR, 2023

Model agnostic methods meta-learn despite misspecifications.

Yüksel Oguz, Etienne Boursier, Nicolas Flammarion

CoRR, 2023

Quantum Channel Certification with Incoherent Strategies.

Omar Fawzi, Nicolas Flammarion, Aurélien Garivier, Aadil Oufkir

CoRR, 2023

(S)GD over Diagonal Linear Networks: Implicit Regularisation, Large Stepsizes and Edge of Stability.

Mathieu Even, Scott Pesme, Suriya Gunasekar, Nicolas Flammarion

CoRR, 2023

On the spectral bias of two-layer linear networks.

Aditya Vardhan Varre, Maria-Luiza Vladarean, Loucas Pillaud-Vivien, Nicolas Flammarion

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Saddle-to-Saddle Dynamics in Diagonal Linear Networks.

Scott Pesme, Nicolas Flammarion

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings.

Klim Kireev, Maksym Andriushchenko, Carmela Troncoso, Nicolas Flammarion

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

(S)GD over Diagonal Linear Networks: Implicit bias, Large Stepsizes and Edge of Stability.

Mathieu Even, Scott Pesme, Suriya Gunasekar, Nicolas Flammarion

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Penalising the biases in norm regularisation enforces sparsity.

Etienne Boursier, Nicolas Flammarion

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Sharpness-Aware Minimization Leads to Low-Rank Features.

Maksym Andriushchenko, Dara Bahri, Hossein Mobahi, Nicolas Flammarion

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

SGD with Large Step Sizes Learns Sparse Features.

Maksym Andriushchenko, Aditya Vardhan Varre, Loucas Pillaud-Vivien, Nicolas Flammarion

Proceedings of the International Conference on Machine Learning, 2023

A Modern Look at the Relationship between Sharpness and Generalization.

Maksym Andriushchenko, Francesco Croce, Maximilian Müller, Matthias Hein, Nicolas Flammarion

Proceedings of the International Conference on Machine Learning, 2023

Linearization Algorithms for Fully Composite Optimization.

Maria-Luiza Vladarean, Nikita Doikov, Martin Jaggi, Nicolas Flammarion

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Quantum Channel Certification with Incoherent Measurements.

Omar Fawzi, Nicolas Flammarion, Aurélien Garivier, Aadil Oufkir

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

An Efficient Sampling Algorithm for Non-smooth Composite Potentials.

Wenlong Mou, Nicolas Flammarion, Martin J. Wainwright, Peter L. Bartlett

J. Mach. Learn. Res., 2022

Sequential algorithms for testing identity and closeness of distributions.

Omar Fawzi, Nicolas Flammarion, Aurélien Garivier, Aadil Oufkir

CoRR, 2022

On the effectiveness of adversarial training against common corruptions.

Klim Kireev, Maksym Andriushchenko, Nicolas Flammarion

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs.

Etienne Boursier, Loucas Pillaud-Vivien, Nicolas Flammarion

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Towards Understanding Sharpness-Aware Minimization.

Maksym Andriushchenko, Nicolas Flammarion

Proceedings of the International Conference on Machine Learning, 2022

ARIA: Adversarially Robust Image Attribution for Content Provenance.

Maksym Andriushchenko, Xiaoyang Rebecca Li, Geoffrey Oxholm, Thomas Gittings, Tu Bui, Nicolas Flammarion, John P. Collomosse

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022

Accelerated SGD for Non-Strongly-Convex Least Squares.

Aditya Varre, Nicolas Flammarion

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Label noise (stochastic) gradient descent implicitly solves the Lasso for quadratic parametrisation.

Loucas Pillaud-Vivien, Julien Reygner, Nicolas Flammarion

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Trace norm regularization for multi-task learning with scarce data.

Etienne Boursier, Mikhail Konobeev, Nicolas Flammarion

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Sparse-RS: A Versatile Framework for Query-Efficient Sparse Black-Box Adversarial Attacks.

Francesco Croce, Maksym Andriushchenko, Naman D. Singh, Nicolas Flammarion, Matthias Hein

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Linear Speedup in Personalized Collaborative Learning.

El Mahdi Chayti, Sai Praneeth Karimireddy, Sebastian U. Stich, Nicolas Flammarion, Martin Jaggi

CoRR, 2021

A Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip.

Mathieu Even, Raphaël Berthier, Francis R. Bach, Nicolas Flammarion, Pierre Gaillard, Hadrien Hendrikx, Laurent Massoulié, Adrien B. Taylor

CoRR, 2021

A Continuized View on Nesterov Acceleration.

Raphaël Berthier, Francis R. Bach, Nicolas Flammarion, Pierre Gaillard, Adrien B. Taylor

CoRR, 2021

Last iterate convergence of SGD for Least-Squares in the Interpolation regime.

Aditya Vardhan Varre, Loucas Pillaud-Vivien, Nicolas Flammarion

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity.

Scott Pesme, Loucas Pillaud-Vivien, Nicolas Flammarion

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Sequential Algorithms for Testing Closeness of Distributions.

Aadil Oufkir, Omar Fawzi, Nicolas Flammarion, Aurélien Garivier

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Continuized Accelerations of Deterministic and Stochastic Gradient Descents, and of Gossip Algorithms.

Mathieu Even, Raphaël Berthier, Francis R. Bach, Nicolas Flammarion, Hadrien Hendrikx, Pierre Gaillard, Laurent Massoulié, Adrien B. Taylor

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

RobustBench: a standardized adversarial robustness benchmark.

Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Edoardo Debenedetti, Nicolas Flammarion, Mung Chiang, Prateek Mittal, Matthias Hein

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

2020

RobustBench: a standardized adversarial robustness benchmark.

Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Nicolas Flammarion, Mung Chiang, Prateek Mittal, Matthias Hein

CoRR, 2020

Optimal Robust Linear Regression in Nearly Linear Time.

Yeshwanth Cherapanamjeri, Efe Aras, Nilesh Tripuraneni, Michael I. Jordan, Nicolas Flammarion, Peter L. Bartlett

CoRR, 2020

Online Robust Regression via SGD on the l1 loss.

Scott Pesme, Nicolas Flammarion

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Understanding and Improving Fast Adversarial Training.

Maksym Andriushchenko, Nicolas Flammarion

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent.

Scott Pesme, Aymeric Dieuleveut, Nicolas Flammarion

Proceedings of the 37th International Conference on Machine Learning, 2020

Square Attack: A Query-Efficient Black-Box Adversarial Attack via Random Search.

Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion, Matthias Hein

Proceedings of the Computer Vision - ECCV 2020, 2020

2019

Is There an Analog of Nesterov Acceleration for MCMC?

Yi-An Ma, Niladri S. Chatterji, Xiang Cheng, Nicolas Flammarion, Peter L. Bartlett, Michael I. Jordan

CoRR, 2019

Escaping from saddle points on Riemannian manifolds.

Yue Sun, Nicolas Flammarion, Maryam Fazel

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Fast Mean Estimation with Sub-Gaussian Rates.

Yeshwanth Cherapanamjeri, Nicolas Flammarion, Peter L. Bartlett

Proceedings of the Conference on Learning Theory, 2019

2018

Sampling Can Be Faster Than Optimization.

Yi-An Ma, Yuansi Chen, Chi Jin, Nicolas Flammarion, Michael I. Jordan

CoRR, 2018

Gen-Oja: A Simple and Efficient Algorithm for Streaming Generalized Eigenvector Computation.

Kush Bhatia, Aldo Pacchiano, Nicolas Flammarion, Peter L. Bartlett, Michael I. Jordan

CoRR, 2018

Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation.

Kush Bhatia, Aldo Pacchiano, Nicolas Flammarion, Peter L. Bartlett, Michael I. Jordan

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo.

Niladri S. Chatterji, Nicolas Flammarion, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan

Proceedings of the 35th International Conference on Machine Learning, 2018

Averaging Stochastic Gradient Descent on Riemannian Manifolds.

Nilesh Tripuraneni, Nicolas Flammarion, Francis R. Bach, Michael I. Jordan

Proceedings of the Conference On Learning Theory, 2018

2017

Stochastic Approximation and Least-Squares Regression, with Applications to Machine Learning. (Approximation Stochastique et Régression par Moindres Carrés : Applications en Apprentissage Automatique).

Nicolas Flammarion

PhD thesis, 2017

Robust Discriminative Clustering with Sparse Regularizers.

Nicolas Flammarion, Balamurugan Palaniappan, Francis R. Bach

J. Mach. Learn. Res., 2017

Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression.

Aymeric Dieuleveut, Nicolas Flammarion, Francis R. Bach

J. Mach. Learn. Res., 2017

Stochastic Composite Least-Squares Regression with Convergence Rate $O(1/n)$.

Nicolas Flammarion, Francis R. Bach

Proceedings of the 30th Conference on Learning Theory, 2017

2015

From Averaging to Acceleration, There is Only a Step-size.

Nicolas Flammarion, Francis R. Bach

Proceedings of The 28th Conference on Learning Theory, 2015