927 citations · 2.7k across the 88 of their papers we have counts for
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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…
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…
Unsupervised Learning from Narrated Instruction Videos
Jean-Baptiste Alayrac, Piotr Bojanowski, Nishant Agrawal +3
We address the problem of automatically learning the main steps to complete a certain task, such as changing a car tire, from a set of narrated instruction videos. The contribution…
Variance Reduced Stochastic Gradient Descent with Neighbors
Thomas Hofmann, Aurelien Lucchi, Simon Lacoste-Julien +1
Stochastic Gradient Descent (SGD) is a workhorse in machine learning, yet its slow convergence can be a computational bottleneck. Variance reduction techniques such as SAG, SVRG an…
Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering
Simon Lacoste-Julien, Fredrik Lindsten, Francis Bach
Recently, the Frank-Wolfe optimization algorithm was suggested as a procedure to obtain adaptive quadrature rules for integrals of functions in a reproducing kernel Hilbert space (…