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
13 papers · 1 filter
Learning Output Embeddings in Structured Prediction
Luc Brogat-Motte, Alessandro Rudi, Céline Brouard +2
A powerful and flexible approach to structured prediction consists in embedding the structured objects to be predicted into a feature space of possibly infinite dimension by means…
Ranking and synchronization from pairwise measurements via SVD
Alexandre d'Aspremont, Mihai Cucuringu, Hemant Tyagi
Given a measurement graph and an unknown signal , we investigate algorithms for recovering from pairwise measurements of the form ; $\…
Regularity as Regularization: Smooth and Strongly Convex Brenier Potentials in Optimal Transport
François-Pierre Paty, Alexandre d'Aspremont, Marco Cuturi
Estimating Wasserstein distances between two high-dimensional densities suffers from the curse of dimensionality: one needs an exponential (wrt dimension) number of samples to ensu…
A Kernel Perspective for Regularizing Deep Neural Networks
Alberto Bietti, Grégoire Mialon, Dexiong Chen +1
We propose a new point of view for regularizing deep neural networks by using the norm of a reproducing kernel Hilbert space (RKHS). Even though this norm cannot be computed, it ad…
Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance
Giulia Luise, Alessandro Rudi, Massimiliano Pontil +1
Applications of optimal transport have recently gained remarkable attention thanks to the computational advantages of entropic regularization. However, in most situations the Sinkh…
Constant Step Size Stochastic Gradient Descent for Probabilistic Modeling
Dmitry Babichev, Francis Bach
Stochastic gradient methods enable learning probabilistic models from large amounts of data. While large step-sizes (learning rates) have shown to be best for least-squares (e.g.,…
Beyond CCA: Moment Matching for Multi-View Models
Anastasia Podosinnikova, Francis Bach, Simon Lacoste-Julien
We introduce three novel semi-parametric extensions of probabilistic canonical correlation analysis with identifiability guarantees. We consider moment matching techniques for esti…
Barrier Frank-Wolfe for Marginal Inference
Rahul G. Krishnan, Simon Lacoste-Julien, David Sontag
We introduce a globally-convergent algorithm for optimizing the tree-reweighted (TRW) variational objective over the marginal polytope. The algorithm is based on the conditional gr…
Rethinking LDA: moment matching for discrete ICA
Anastasia Podosinnikova, Francis Bach, Simon Lacoste-Julien
We consider moment matching techniques for estimation in Latent Dirichlet Allocation (LDA). By drawing explicit links between LDA and discrete versions of independent component ana…
A New PAC-Bayesian Perspective on Domain Adaptation
Pascal Germain, Amaury Habrard, François Laviolette +1
We study the issue of PAC-Bayesian domain adaptation: We want to learn, from a source domain, a majority vote model dedicated to a target one. Our theoretical contribution brings a…
PAC-Bayesian Theorems for Domain Adaptation with Specialization to Linear Classifiers
Pascal Germain, Amaury Habrard, François Laviolette +1
In this paper, we provide two main contributions in PAC-Bayesian theory for domain adaptation where the objective is to learn, from a source distribution, a well-performing majorit…
Learning the Structure for Structured Sparsity
Nino Shervashidze, Francis Bach
Structured sparsity has recently emerged in statistics, machine learning and signal processing as a promising paradigm for learning in high-dimensional settings. All existing metho…
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…