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Highly Cited

2016

Highly Cited

2016

We give a simple proof that the Frank-Wolfe algorithm obtains a stationary point at a rate of $O(1/\sqrt{t})$ on non-convex… Expand

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Highly Cited

2016

Highly Cited

2016

We present new results for the Frank–Wolfe method (also known as the conditional gradient method). We derive computational… Expand

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2016

2016

We study parallel and distributed Frank-Wolfe algorithms; the former on shared memory machines with mini-batching, and the latter… Expand

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Highly Cited

2015

Highly Cited

2015

The Frank-Wolfe (FW) optimization algorithm has lately re-gained popularity thanks in particular to its ability to nicely handle… Expand

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Highly Cited

2015

Highly Cited

2015

The Frank-Wolfe method (a.k.a. conditional gradient algorithm) for smooth optimization has regained much interest in recent years… Expand

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Highly Cited

2015

Highly Cited

2015

There is renewed interest in formulating integration as a statistical inference problem, motivated by obtaining a full… Expand

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2015

2015

Learning sparse combinations is a frequent theme in machine learning. In this paper, we study its associated optimization problem… Expand

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Highly Cited

2014

Highly Cited

2014

In this paper, we tackle the problem of performing efficient co-localization in images and videos. Co-localization is the problem… Expand

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Highly Cited

2013

Highly Cited

2013

We provide stronger and more general primal-dual convergence results for Frank-Wolfe-type algorithms (a.k.a. conditional gradient… Expand

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Highly Cited

2013

Highly Cited

2013

We study the linear convergence of variants of the Frank-Wolfe algorithms for some classes of strongly convex problems, using… Expand

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