604 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- École Normale Supérieure - PSLFR41 papers
- Institut national de recherche en sciences et technologies du numériqueFR30 papers
- Département d'InformatiqueFR17 papers
- Centre National de la Recherche ScientifiqueFR6 papers
- Université Paris Sciences et LettresFR5 papers
- CentraleSupélecFR3 papers
- École Normale SupérieureBI3 papers
- Institut Polytechnique de ParisFR3 papers
- Laboratoire Traitement et Communication de l’InformationFR3 papers
- Massachusetts Institute of TechnologyUS3 papers
- CEA Paris-Saclay - Etablissement de SaclayFR2 papers
- Centre de Vision NumériqueFR2 papers
5 papers · 1 filter
Regret lower bounds and extended Upper Confidence Bounds policies in stochastic multi-armed bandit problem
Antoine Salomon, Jean-Yves Audibert, Issam El Alaoui
This paper is devoted to regret lower bounds in the classical model of stochastic multi-armed bandit. A well-known result of Lai and Robbins, which has then been extended by Burnet…
Low-rank optimization with trace norm penalty
B. Mishra, G. Meyer, F. Bach +1
The paper addresses the problem of low-rank trace norm minimization. We propose an algorithm that alternates between fixed-rank optimization and rank-one updates. The fixed-rank op…
Trace Lasso: a trace norm regularization for correlated designs
Edouard Grave, Guillaume Obozinski, Francis Bach
Using the -norm to regularize the estimation of the parameter vector of a linear model leads to an unstable estimator when covariates are highly correlated. In this paper,…
Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization
Mark Schmidt, Nicolas Le Roux, Francis Bach
We consider the problem of optimizing the sum of a smooth convex function and a non-smooth convex function using proximal-gradient methods, where an error is present in the calcula…
Multi-task Regression using Minimal Penalties
Matthieu Solnon, Sylvain Arlot, Francis Bach
In this paper we study the kernel multiple ridge regression framework, which we refer to as multi-task regression, using penalization techniques. The theoretical analysis of this p…