Fast Stochastic Frank-Wolfe Algorithms for Nonlinear SVMs

By Hua Ouyang, Alexander G. Gray. 2010

28 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Optimization & Training, Signal Processing

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Citations in the corpus

Block-Coordinate Frank-Wolfe Optimization for Structural SVMs (2012, methodology): “…paper we consider the Frank-Wolfe (1956) algorithm, which has seen a recent surge of interest in machine learning and signal processing (Mangasarian, 1995; Clarkson, 2010; Jaggi, 2011; 2013; Bach et al., 2012), including in the context of binary SVMs (Gärtner & Jaggi, 2009; Ouyang & Gray, 2010).”
A Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning (2014, background): “The dual problem of L2-loss SVM with kernel k is as follows (Tsang et al., 2005; Ouyang and Gray, 2010): min α∈∆n αTK̃α, (4) with K̃ = [k̃(zi, zj)]ni,j=1 where k̃(zi, zj) = yiyjk(xi,xj) + yiyj + δij C is the “augmented kernel”.”
Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms (2014, unknown): “…et al., 2009; Harchaoui et al., 2015; Zhang et al., 2013), linearly convergent special cases (Garber & Hazan, 2013; Lacoste-Julien & Jaggi, 2015), stochastic versions (Hazan & Kale, 2012; Lafond et al., 2015; Ouyang & Gray, 2010), and a randomized block-coordinate FW (Lacoste-Julien et al., 2013).”
D-FW: Communication efficient distributed algorithms for high-dimensional sparse optimization (2016, methodology): “The Frank-Wolfe algorithm [12] is recently rediscovered as an appealing tool to handle large-scale convex optimization and it has been applied to a number of practical problems [13–16]; see the surveys [17, 18].”
A novel Frank-Wolfe algorithm. Analysis and applications to large-scale SVM training (2013, methodology): “In [44], Ouyang and Gray propose a stochastic variant of FW methods for online learning of L2-SVMs, obtaining comparable and sometimes better accuracies than state-of-the-art batch and online algorithms for training SVMs.”
Revisiting Frank-Wolfe: Projection-Free Sparse Convex Optimization (2013, background): “…, 1996), sparse recovery (Mallat & Zhang, 1993), and many learning tasks, where e.g. boosting (Adaboost ), support vector machines (Gärtner & Jaggi, 2009; Clarkson, 2010; Ouyang & Gray, 2010), and density estimation (Li & Barron, 2000; Bach et al., 2012) turn out to be such problem instances.”