The Frank–Wolfe algorithm is an iterative first-order optimization algorithm for constrained convex optimization. Also known as the conditional… (More)

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Review

2017

Review

2017

- Gauthier Gidel, Tony Jebara, Simon Lacoste-Julien
- AISTATS
- 2017

We extend the Frank-Wolfe (FW) optimization algorithm to solve constrained smooth convex-concave saddle point (SP) problems… (More)

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2016

2016

- Christian Bauckhage
- LWDA
- 2016

We show that k-means clustering is a matrix factorization problem. Seen from this point of view, k-means clustering can be… (More)

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2016

2016

- Yu-Xiang Wang, Veeranjaneyulu Sadhanala, Wei Dai, Willie Neiswanger, Suvrit Sra, Eric P. Xing
- ICML
- 2016

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

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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… (More)

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2015

2015

- Javier Peña, Daniel Rodŕıguez
- 2015

It is known that the gradient descent algorithm converges linearly when applied to a strongly convex function with Lipschitz… (More)

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Highly Cited

2014

Highly Cited

2014

- Armand Joulin, Kevin D. Tang, Li Fei-Fei
- ECCV
- 2014

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

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Highly Cited

2013

Highly Cited

2013

- Martin Jaggi
- ICML
- 2013

We provide stronger and more general primal-dual convergence results for FrankWolfe-type algorithms (a.k.a. conditional gradient… (More)

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Highly Cited

2013

Highly Cited

2013

We propose a randomized block-coordinate variant of the classic Frank-Wolfe algorithm for convex optimization with block… (More)

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2010

2010

- Hua Ouyang, Alexander G. Gray
- SDM
- 2010

The high computational cost of nonlinear support vector machines has limited their usability for large-scale problems. We propose… (More)

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Review

2008

Review

2008

- Kenneth L. Clarkson
- SODA
- 2008

The problem of maximizing a concave function <i>f(x)</i> in the unit simplex Δ can be solved approximately by a simple… (More)

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