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
14 papers · 1 filter
On the Consistency of Max-Margin Losses
Alex Nowak-Vila, Alessandro Rudi, Francis Bach
The foundational concept of Max-Margin in machine learning is ill-posed for output spaces with more than two labels such as in structured prediction. In this paper, we show that th…
Partially Encrypted Machine Learning using Functional Encryption
Theo Ryffel, Edouard Dufour-Sans, Romain Gay +2
Machine learning on encrypted data has received a lot of attention thanks to recent breakthroughs in homomorphic encryption and secure multi-party computation. It allows outsourcin…
Sharp Analysis of Learning with Discrete Losses
Alex Nowak-Vila, Francis Bach, Alessandro Rudi
The problem of devising learning strategies for discrete losses (e.g., multilabeling, ranking) is currently addressed with methods and theoretical analyses ad-hoc for each loss. In…
Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs
Anton Osokin, Jean-Baptiste Alayrac, Isabella Lukasewitz +2
In this paper, we propose several improvements on the block-coordinate Frank-Wolfe (BCFW) algorithm from Lacoste-Julien et al. (2013) recently used to optimize the structured suppo…
Submodular Functions: from Discrete to Continous Domains
Francis Bach
Submodular set-functions have many applications in combinatorial optimization, as they can be minimized and approximately maximized in polynomial time. A key element in many of the…
Tensorizing Neural Networks
Alexander Novikov, Dmitry Podoprikhin, Anton Osokin +1
Deep neural networks currently demonstrate state-of-the-art performance in several domains. At the same time, models of this class are very demanding in terms of computational reso…
Breaking the Curse of Dimensionality with Convex Neural Networks
Francis Bach
We consider neural networks with a single hidden layer and non-decreasing homogeneous activa-tion functions like the rectified linear units. By letting the number of hidden units g…
On the Consistency of Ordinal Regression Methods
Fabian Pedregosa, Francis Bach, Alexandre Gramfort
Many of the ordinal regression models that have been proposed in the literature can be seen as methods that minimize a convex surrogate of the zero-one, absolute, or squared loss f…
Spectral Ranking using Seriation
Fajwel Fogel, Alexandre d'Aspremont, Milan Vojnovic
We describe a seriation algorithm for ranking a set of items given pairwise comparisons between these items. Intuitively, the algorithm assigns similar rankings to items that compa…
Convex relaxations of structured matrix factorizations
Francis Bach
We consider the factorization of a rectangular matrix into a positive linear combination of rank-one factors of the form , where and belongs to certain sets…
Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n)
Francis Bach, Eric Moulines
We consider the stochastic approximation problem where a convex function has to be minimized, given only the knowledge of unbiased estimates of its gradients at certain points, a f…
Sharp analysis of low-rank kernel matrix approximations
Francis Bach
We consider supervised learning problems within the positive-definite kernel framework, such as kernel ridge regression, kernel logistic regression or the support vector machine. W…
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…