1 code implementation • 15 Dec 2023 • Waïss Azizian, Guillaume Baudart, Marc Lelarge
Exact Bayesian inference on state-space models~(SSM) is in general untractable, and unfortunately, basic Sequential Monte Carlo~(SMC) methods do not yield correct approximations for complex models.
no code implementations • 15 Nov 2022 • Waïss Azizian, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos
For generality, we focus on local solutions of constrained, non-monotone variational inequalities, and we show that the convergence rate of a given method depends sharply on its associated Legendre exponent, a notion that measures the growth rate of the underlying Bregman function (Euclidean, entropic, or other) near a solution.
no code implementations • 5 Jul 2021 • Waïss Azizian, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos
In this paper, we analyze the local convergence rate of optimistic mirror descent methods in stochastic variational inequalities, a class of optimization problems with important applications to learning theory and machine learning.
1 code implementation • ICLR 2021 • Waïss Azizian, Marc Lelarge
Various classes of Graph Neural Networks (GNN) have been proposed and shown to be successful in a wide range of applications with graph structured data.
no code implementations • 2 Jan 2020 • Waïss Azizian, Damien Scieur, Ioannis Mitliagkas, Simon Lacoste-Julien, Gauthier Gidel
Using this perspective, we propose an optimal algorithm for bilinear games.
no code implementations • ICML 2020 • Adam Ibrahim, Waïss Azizian, Gauthier Gidel, Ioannis Mitliagkas
In this work, we approach the question of fundamental iteration complexity by providing lower bounds to complement the linear (i. e. geometric) upper bounds observed in the literature on a wide class of problems.
no code implementations • 13 Jun 2019 • Waïss Azizian, Ioannis Mitliagkas, Simon Lacoste-Julien, Gauthier Gidel
We provide new analyses of the EG's local and global convergence properties and use is to get a tighter global convergence rate for OG and CO. Our analysis covers the whole range of settings between bilinear and strongly monotone games.