activity
20172021
most citedNonlinear Acceleration of Stochastic Algorithms

17 citations · 36 across the 7 of their papers we have counts for

collaborators

13 papers

cs.CV2021

Connecting Sphere Manifolds Hierarchically for Regularization

Damien Scieur, Youngsung Kim

This paper considers classification problems with hierarchically organized classes. We force the classifier (hyperplane) of each class to belong to a sphere manifold, whose center…

math.OC20204 cited

Affine Invariant Analysis of Frank-Wolfe on Strongly Convex Sets

Thomas Kerdreux, Lewis Liu, Simon Lacoste-Julien +1

It is known that the Frank-Wolfe (FW) algorithm, which is affine-covariant, enjoys accelerated convergence rates when the constraint set is strongly convex. However, these results…

math.OC2020

Generalization of Quasi-Newton Methods: Application to Robust Symmetric Multisecant Updates

Damien Scieur, Lewis Liu, Thomas Pumir +1

Quasi-Newton techniques approximate the Newton step by estimating the Hessian using the so-called secant equations. Some of these methods compute the Hessian using several secant e…

math.OC20201 cited

Average-case Acceleration for Bilinear Games and Normal Matrices

Carles Domingo-Enrich, Fabian Pedregosa, Damien Scieur

Advances in generative modeling and adversarial learning have given rise to renewed interest in smooth games. However, the absence of symmetry in the matrix of second derivatives p…

math.OC2020

Universal Average-Case Optimality of Polyak Momentum

Damien Scieur, Fabian Pedregosa

Polyak momentum (PM), also known as the heavy-ball method, is a widely used optimization method that enjoys an asymptotic optimal worst-case complexity on quadratic objectives. How…

cs.LG2020

Accelerating Smooth Games by Manipulating Spectral Shapes

Waïss Azizian, Damien Scieur, Ioannis Mitliagkas +2

We use matrix iteration theory to characterize acceleration in smooth games. We define the spectral shape of a family of games as the set containing all eigenvalues of the Jacobian…