40 citations
- Laboratoire Traitement et Communication de l’InformationFR14 papers
- KU LeuvenBE13 papers
- Institut Polytechnique de ParisFR7 papers
- Laboratoire Traitement du Signal et de l'ImageFR4 papers
- Télécom ParisFR4 papers
- Centre National de la Recherche ScientifiqueFR3 papers
- Laboratoire d’Informatique et SystèmesFR3 papers
- Centre BorelliFR2 papers
- IBM (France)FR2 papers
- Institut national de recherche en sciences et technologies du numériqueFR2 papers
- Kyoto UniversityJP2 papers
- Laboratoire des signaux et systèmesFR2 papers
5 papers · 1 filter
Linear convergence of dual coordinate descent on non-polyhedral convex problems
Ion Necoara, Olivier Fercoq
This paper deals with constrained convex problems, where the objective function is smooth strongly convex and the feasible set is given as the intersection of a large number of clo…
First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise
Thanh Huy Nguyen, Umut Şimşekli, Mert Gürbüzbalaban +1
Stochastic gradient descent (SGD) has been widely used in machine learning due to its computational efficiency and favorable generalization properties. Recently, it has been empiri…
Generalized Sliced Wasserstein Distances
Soheil Kolouri, Kimia Nadjahi, Umut Simsekli +2
The Wasserstein distance and its variations, e.g., the sliced-Wasserstein (SW) distance, have recently drawn attention from the machine learning community. The SW distance, specifi…
Almost surely constrained convex optimization
Olivier Fercoq, Ahmet Alacaoglu, Ion Necoara +1
We propose a stochastic gradient framework for solving stochastic composite convex optimization problems with (possibly) infinite number of linear inclusion constraints that need t…
A Conditional Gradient-Based Augmented Lagrangian Framework
Alp Yurtsever, Olivier Fercoq, Volkan Cevher
This paper considers a generic convex minimization template with affine constraints over a compact domain, which covers key semidefinite programming applications. The existing cond…