
Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity
Understanding the implicit bias of training algorithms is of crucial imp...
read it

A Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip
We introduce the continuized Nesterov acceleration, a close variant of N...
read it

On the effectiveness of adversarial training against common corruptions
The literature on robustness towards common corruptions shows no consens...
read it

A Continuized View on Nesterov Acceleration
We introduce the "continuized" Nesterov acceleration, a close variant of...
read it

Last iterate convergence of SGD for LeastSquares in the Interpolation regime
Motivated by the recent successes of neural networks that have the abili...
read it

RobustBench: a standardized adversarial robustness benchmark
Evaluation of adversarial robustness is often errorprone leading to ove...
read it

Optimal Robust Linear Regression in Nearly Linear Time
We study the problem of highdimensional robust linear regression where ...
read it

Understanding and Improving Fast Adversarial Training
A recent line of work focused on making adversarial training computation...
read it

On ConvergenceDiagnostic based Step Sizes for Stochastic Gradient Descent
Constant stepsize Stochastic Gradient Descent exhibits two phases: a tr...
read it

Online Robust Regression via SGD on the l1 loss
We consider the robust linear regression problem in the online setting w...
read it

SparseRS: a versatile framework for queryefficient sparse blackbox adversarial attacks
A large body of research has focused on adversarial attacks which requir...
read it

Square Attack: a queryefficient blackbox adversarial attack via random search
We propose the Square Attack, a new scorebased blackbox l_2 and l_∞ ad...
read it

An Efficient Sampling Algorithm for Nonsmooth Composite Potentials
We consider the problem of sampling from a density of the form p(x) ∝(f...
read it

Improved Bounds for Discretization of Langevin Diffusions: NearOptimal Rates without Convexity
We present an improved analysis of the EulerMaruyama discretization of ...
read it

Escaping from saddle points on Riemannian manifolds
We consider minimizing a nonconvex, smooth function f on a Riemannian ma...
read it

Fast Mean Estimation with SubGaussian Rates
We propose an estimator for the mean of a random vector in R^d that can ...
read it

Is There an Analog of Nesterov Acceleration for MCMC?
We formulate gradientbased Markov chain Monte Carlo (MCMC) sampling as ...
read it

Sampling Can Be Faster Than Optimization
Optimization algorithms and Monte Carlo sampling algorithms have provide...
read it

GenOja: A Simple and Efficient Algorithm for Streaming Generalized Eigenvector Computation
In this paper, we study the problems of principal Generalized Eigenvecto...
read it

Averaging Stochastic Gradient Descent on Riemannian Manifolds
We consider the minimization of a function defined on a Riemannian manif...
read it

On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo
We provide convergence guarantees in Wasserstein distance for a variety ...
read it

Stochastic Composite LeastSquares Regression with convergence rate O(1/n)
We consider the minimization of composite objective functions composed o...
read it

Robust Discriminative Clustering with Sparse Regularizers
Clustering highdimensional data often requires some form of dimensional...
read it

Optimal Rates of Statistical Seriation
Given a matrix the seriation problem consists in permuting its rows in s...
read it

Harder, Better, Faster, Stronger Convergence Rates for LeastSquares Regression
We consider the optimization of a quadratic objective function whose gra...
read it

From Averaging to Acceleration, There is Only a Stepsize
We show that accelerated gradient descent, averaged gradient descent and...
read it
Nicolas Flammarion
is this you? claim profile