Nicolas Flammarion

research

∙ 06/06/2023

Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings

Research on adversarial robustness is primarily focused on image and tex...

0 Klim Kireev, et al. ∙

research

∙ 05/25/2023

Sharpness-Aware Minimization Leads to Low-Rank Features

Sharpness-aware minimization (SAM) is a recently proposed method that mi...

1 Maksym Andriushchenko, et al. ∙

research

∙ 04/02/2023

Saddle-to-Saddle Dynamics in Diagonal Linear Networks

In this paper we fully describe the trajectory of gradient flow over dia...

0 Scott Pesme, et al. ∙

research

∙ 03/02/2023

Penalising the biases in norm regularisation enforces sparsity

Controlling the parameters' norm often yields good generalisation when t...

0 Etienne Boursier, et al. ∙

research

∙ 03/02/2023

Model agnostic methods meta-learn despite misspecifications

Due to its empirical success on few shot classification and reinforcemen...

0 Oguz Yuksel, et al. ∙

research

∙ 03/02/2023

Quantum Channel Certification with Incoherent Strategies

In the problem of quantum channel certification, we have black box acces...

0 Omar Fawzi, et al. ∙

research

∙ 02/24/2023

Linearization Algorithms for Fully Composite Optimization

In this paper, we study first-order algorithms for solving fully composi...

0 Maria-Luiza Vladarean, et al. ∙

research

∙ 02/17/2023

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

In this paper, we investigate the impact of stochasticity and large step...

0 Mathieu Even, et al. ∙

research

∙ 02/14/2023

A modern look at the relationship between sharpness and generalization

Sharpness of minima is a promising quantity that can correlate with gene...

0 Maksym Andriushchenko, et al. ∙

research

∙ 10/11/2022

SGD with large step sizes learns sparse features

We showcase important features of the dynamics of the Stochastic Gradien...

0 Maksym Andriushchenko, et al. ∙

research

∙ 06/20/2022

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

Understanding the implicit bias of training algorithms is of crucial imp...

0 Loucas Pillaud-Vivien, et al. ∙

research

∙ 06/13/2022

Towards Understanding Sharpness-Aware Minimization

Sharpness-Aware Minimization (SAM) is a recent training method that reli...

0 Maksym Andriushchenko, et al. ∙

research

∙ 06/02/2022

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

The training of neural networks by gradient descent methods is a corners...

0 Etienne Boursier, et al. ∙

research

∙ 05/12/2022

Sequential algorithms for testing identity and closeness of distributions

What advantage do sequential procedures provide over batch algorithms fo...

0 Omar Fawzi, et al. ∙

research

∙ 03/03/2022

Accelerated SGD for Non-Strongly-Convex Least Squares

We consider stochastic approximation for the least squares regression pr...

0 Aditya Varre, et al. ∙

research

∙ 02/25/2022

ARIA: Adversarially Robust Image Attribution for Content Provenance

Image attribution – matching an image back to a trusted source – is an e...

0 Maksym Andriushchenko, et al. ∙

research

∙ 02/14/2022

Trace norm regularization for multi-task learning with scarce data

Multi-task learning leverages structural similarities between multiple t...

0 Etienne Boursier, et al. ∙

research

∙ 11/10/2021

Linear Speedup in Personalized Collaborative Learning

Personalization in federated learning can improve the accuracy of a mode...

0 El Mahdi Chayti, et al. ∙

research

∙ 06/17/2021

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

Understanding the implicit bias of training algorithms is of crucial imp...

0 Scott Pesme, et al. ∙

research

∙ 06/10/2021

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

We introduce the continuized Nesterov acceleration, a close variant of N...

0 Mathieu Even, et al. ∙

research

∙ 03/03/2021

On the effectiveness of adversarial training against common corruptions

The literature on robustness towards common corruptions shows no consens...

0 Klim Kireev, et al. ∙

research

∙ 02/11/2021

A Continuized View on Nesterov Acceleration

We introduce the "continuized" Nesterov acceleration, a close variant of...

0 Raphaël Berthier, et al. ∙

research

∙ 02/05/2021

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

Motivated by the recent successes of neural networks that have the abili...

0 Aditya Varre, et al. ∙

research

∙ 10/19/2020

RobustBench: a standardized adversarial robustness benchmark

Evaluation of adversarial robustness is often error-prone leading to ove...

4 Francesco Croce, et al. ∙

research

∙ 07/16/2020

Optimal Robust Linear Regression in Nearly Linear Time

We study the problem of high-dimensional robust linear regression where ...

0 Yeshwanth Cherapanamjeri, et al. ∙

research

∙ 07/06/2020

Understanding and Improving Fast Adversarial Training

A recent line of work focused on making adversarial training computation...

0 Maksym Andriushchenko, et al. ∙

research

∙ 07/01/2020

On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent

Constant step-size Stochastic Gradient Descent exhibits two phases: a tr...

0 Scott Pesme, et al. ∙

research

∙ 07/01/2020

Online Robust Regression via SGD on the l1 loss

We consider the robust linear regression problem in the online setting w...

0 Scott Pesme, et al. ∙

research

∙ 06/23/2020

Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks

A large body of research has focused on adversarial attacks which requir...

0 Francesco Croce, et al. ∙

research

∙ 11/29/2019

Square Attack: a query-efficient black-box adversarial attack via random search

We propose the Square Attack, a new score-based black-box l_2 and l_∞ ad...

0 Maksym Andriushchenko, et al. ∙

research

∙ 10/01/2019

An Efficient Sampling Algorithm for Non-smooth Composite Potentials

We consider the problem of sampling from a density of the form p(x) ∝(-f...

24 Wenlong Mou, et al. ∙

research

∙ 07/25/2019

Improved Bounds for Discretization of Langevin Diffusions: Near-Optimal Rates without Convexity

We present an improved analysis of the Euler-Maruyama discretization of ...

1 Wenlong Mou, et al. ∙

research

∙ 06/18/2019

Escaping from saddle points on Riemannian manifolds

We consider minimizing a nonconvex, smooth function f on a Riemannian ma...

0 Yue Sun, et al. ∙

research

∙ 02/06/2019

Fast Mean Estimation with Sub-Gaussian Rates

We propose an estimator for the mean of a random vector in R^d that can ...

0 Yeshwanth Cherapanamjeri, et al. ∙

research

∙ 02/04/2019

Is There an Analog of Nesterov Acceleration for MCMC?

We formulate gradient-based Markov chain Monte Carlo (MCMC) sampling as ...

4 Yi-an Ma, et al. ∙

research

∙ 11/20/2018

Sampling Can Be Faster Than Optimization

Optimization algorithms and Monte Carlo sampling algorithms have provide...

0 Yi-an Ma, et al. ∙

research

∙ 11/20/2018

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

In this paper, we study the problems of principal Generalized Eigenvecto...

0 Kush Bhatia, et al. ∙

research

∙ 02/26/2018

Averaging Stochastic Gradient Descent on Riemannian Manifolds

We consider the minimization of a function defined on a Riemannian manif...

0 Nilesh Tripuraneni, et al. ∙

research

∙ 02/15/2018

On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo

We provide convergence guarantees in Wasserstein distance for a variety ...

0 Niladri S. Chatterji, et al. ∙

research

∙ 02/21/2017

Stochastic Composite Least-Squares Regression with convergence rate O(1/n)

We consider the minimization of composite objective functions composed o...

0 Nicolas Flammarion, et al. ∙

research

∙ 08/29/2016

Robust Discriminative Clustering with Sparse Regularizers

Clustering high-dimensional data often requires some form of dimensional...

0 Nicolas Flammarion, et al. ∙

research

∙ 07/08/2016

Optimal Rates of Statistical Seriation

Given a matrix the seriation problem consists in permuting its rows in s...

0 Nicolas Flammarion, et al. ∙

research

∙ 02/17/2016

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

We consider the optimization of a quadratic objective function whose gra...

0 Aymeric Dieuleveut, et al. ∙

research

∙ 04/07/2015

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

We show that accelerated gradient descent, averaged gradient descent and...

0 Nicolas Flammarion, et al. ∙

Nicolas Flammarion

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