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
10 papers · 1 filter
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…
On the Global Linear Convergence of Frank-Wolfe Optimization Variants
Simon Lacoste-Julien, Martin Jaggi
The Frank-Wolfe (FW) optimization algorithm has lately re-gained popularity thanks in particular to its ability to nicely handle the structured constraints appearing in machine lea…
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…
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…
A Riemannian low-rank method for optimization over semidefinite matrices with block-diagonal constraints
Nicolas Boumal
We propose a new algorithm to solve optimization problems of the form for a smooth function under the constraints that is positive semidefinite and the diagonal…
Batched bandit problems
Vianney Perchet, Philippe Rigollet, Sylvain Chassang +1
Motivated by practical applications, chiefly clinical trials, we study the regret achievable for stochastic bandits under the constraint that the employed policy must split trials…
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…