26 citations · 133 across the 25 of their papers we have counts for
31 papers
What is the long-run distribution of stochastic gradient descent? A large deviations analysis
Waïss Azizian, Franck Iutzeler, Jérôme Malick +1
In this paper, we examine the long-run distribution of stochastic gradient descent (SGD) in general, non-convex problems. Specifically, we seek to understand which regions of the p…
Exact Generalization Guarantees for (Regularized) Wasserstein Distributionally Robust Models
Waïss Azizian, Franck Iutzeler, Jérôme Malick
Wasserstein distributionally robust estimators have emerged as powerful models for prediction and decision-making under uncertainty. These estimators provide attractive generalizat…
The rate of convergence of Bregman proximal methods: Local geometry vs. regularity vs. sharpness
Waïss Azizian, Franck Iutzeler, Jérôme Malick +1
We examine the last-iterate convergence rate of Bregman proximal methods - from mirror descent to mirror-prox and its optimistic variants - as a function of the local geometry indu…
Harnessing structure in composite nonsmooth minimization
Gilles Bareilles, Franck Iutzeler, Jérôme Malick
We consider the problem of minimizing the composition of a nonsmooth function with a smooth mapping in the case where the proximity operator of the nonsmooth function can be explic…
Push--Pull with Device Sampling
Yu-Guan Hsieh, Yassine Laguel, Franck Iutzeler +1
We consider decentralized optimization problems in which a number of agents collaborate to minimize the average of their local functions by exchanging over an underlying communicat…
Regularization for Wasserstein Distributionally Robust Optimization
Waïss Azizian, Franck Iutzeler, Jérôme Malick
Optimal transport has recently proved to be a useful tool in various machine learning applications needing comparisons of probability measures. Among these, applications of distrib…