A Tight and Unified Analysis of Extragradient for a Whole Spectrum of Differentiable Games. - Waïss Azizian; Ioannis Mitliagkas; Simon Lacoste-Julien; Gauthier Gidel (CoRR, 2019)

Lower Bounds and Conditioning of Differentiable Games. - Adam Ibrahim; Waïss Azizian; Gauthier Gidel; Ioannis Mitliagkas (CoRR, 2019)

A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Differentiable Games. - Waïss Azizian; Ioannis Mitliagkas; Simon Lacoste-Julien; Gauthier Gidel (AISTATS, 2020)

Accelerating Smooth Games by Manipulating Spectral Shapes. - Waïss Azizian; Damien Scieur; Ioannis Mitliagkas; Simon Lacoste-Julien; Gauthier Gidel (AISTATS, 2020)

Linear Lower Bounds and Conditioning of Differentiable Games. - Adam Ibrahim; Waïss Azizian; Gauthier Gidel; Ioannis Mitliagkas (ICML, 2020)

Characterizing the Expressive Power of Invariant and Equivariant Graph Neural Networks. - Waïss Azizian; Marc Lelarge (CoRR, 2020)

The Last-Iterate Convergence Rate of Optimistic Mirror Descent in Stochastic Variational Inequalities. - Waïss Azizian; Franck Iutzeler; Jérôme Malick; Panayotis Mertikopoulos (COLT, 2021)

Expressive Power of Invariant and Equivariant Graph Neural Networks. - Waïss Azizian; Marc Lelarge (ICLR, 2021)

On the rate of convergence of Bregman proximal methods in constrained variational inequalities. - Waïss Azizian; Franck Iutzeler; Jérôme Malick; Panayotis Mertikopoulos (CoRR, 2022)

Exact Generalization Guarantees for (Regularized) Wasserstein Distributionally Robust Models. - Waïss Azizian; Franck Iutzeler; Jérôme Malick (NeurIPS, 2023)

Automatic Rao-Blackwellization for Sequential Monte Carlo with Belief Propagation. - Waïss Azizian; Guillaume Baudart; Marc Lelarge (CoRR, 2023)

What is the Long-Run Distribution of Stochastic Gradient Descent? A Large Deviations Analysis. - Waïss Azizian; Franck Iutzeler; Jérôme Malick; Panayotis Mertikopoulos (ICML, 2024)

Almost sure convergence rates of stochastic gradient methods under gradient domination. - Simon Weissmann; Sara Klein; Waïss Azizian; Leif Döring (CoRR, 2024)

skwdro: a library for Wasserstein distributionally robust machine learning. - Florian Vincent; Waïss Azizian; Franck Iutzeler; Jérôme Malick (CoRR, 2024)

The Rate of Convergence of Bregman Proximal Methods: Local Geometry Versus Regularity Versus Sharpness. - Waïss Azizian; Franck Iutzeler; Jérôme Malick; Panayotis Mertikopoulos (SIAM J. Optim., 2024)

Almost Sure Convergence of Stochastic Gradient Methods under Gradient Domination. - Simon Weissmann; Sara Klein; Waïss Azizian; Leif Döring (Trans. Mach. Learn. Res., 2025)

The global convergence time of stochastic gradient descent in non-convex landscapes: Sharp estimates via large deviations. - Waïss Azizian; Franck Iutzeler; Jérôme Malick; Panayotis Mertikopoulos (CoRR, 2025)

The Geometries of Truth Are Orthogonal Across Tasks. - Waïss Azizian; Michael Kirchhof; Eugène Ndiaye; Louis Béthune; Michal Klein; Pierre Ablin; Marco Cuturi (CoRR, 2025)