Fetching the paper…
Reading the bibliography…
We propose a randomized block-coordinate variant of the classic Frank-Wolfe algorithm for convex optimization with block-separable constraints.
An algorithm for quadratic programming
Frank, M. and Wolfe, P · 1956
Earlier work this paper cites.
Conditional gradient algorithms with open loop step size rules
Dunn, J.C. and Harshbarger, S · 1978
Earlier work this paper cites.
On the convergence of the coordinate descent method for convex differentiable minimization
Luo, Z Q and Tseng, P · 1992
Earlier work this paper cites.
Machine learning via polyhedral concave minimization
Mangasarian, O.L · 1995
Earlier work this paper cites.
Decomposition methods for differentiable optimization problems over cartesian product sets
Patriksson, M · 1998
Earlier work this paper cites.
Introduction to the CoNLL-2000 shared task: Chunking, 2000
Sang, E.F.T.K. and Buchholz, S · 2000
Earlier work this paper cites.
Max-margin Markov networks
Taskar, B., Guestrin, C., and Koller, D · 2003
Earlier work this paper cites.
Learning structured prediction models: A large margin approach
Taskar, B · 2004
Earlier work this paper cites.
Convex optimization
Boyd, S. and Vandenberghe, L · 2004
Earlier work this paper cites.
Large margin methods for structured and interdependent output variables
Tsochantaridis, I., Joachims, T., Hofmann, T., and Altun, Y · 2005
Earlier work this paper cites.
Kernel-based learning of hierarchical multilabel classification models
Rousu, J., Saunders, C., Szedmak, S., and Shawe-Taylor, J · 2006
Earlier work this paper cites.
Structured prediction, dual extragradient and Bregman projections
Taskar, B., Lacoste-Julien, S., and Jordan, M. I · 2006
Earlier work this paper cites.
Convex analysis and nonlinear optimization: theory and examples
Borwein, J. and Lewis, A · 2006
Cited alongside, same era.
(Online) subgradient methods for structured prediction
Ratliff, N., Bagnell, J. A., and Zinkevich, M · 2007
Cited alongside, same era.
A scalable modular convex solver for regularized risk minimization
Teo, C.H., Smola, A.J., Vishwanathan, SVN, and Le, Q.V · 2007
Cited alongside, same era.
Exponentiated gradient algorithms for conditional random fields and max-margin Markov networks
Collins, M., Globerson, A., Koo, T., Carreras, X., and Bartlett, P. L · 2008
Cited alongside, same era.
Training structural SVMs when exact inference is intractable
Finley, T. and Joachims, T · 2008
Cited alongside, same era.
A dual coordinate descent method for large-scale linear SVM
Hsieh, C., Chang, K., Lin, C., Keerthi, S., and Sundararajan, S · 2008
A sequential dual method for structural SVMs
Balamurugan, P., Shevade, S., Sundararajan, S., and Keerthi, S · 2011
Later among the works it cites.
Sparse convex optimization methods for machine learning
Jaggi, M · 2011
Later among the works it cites.
Accelerated training of max-margin Markov networks with kernels
Zhang, X., Saha, A., and Vishwanathan, S. V. N · 2011
Later among the works it cites.
Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function
Richtárik, P. and Takáč, M · 2011
Later among the works it cites.
A MATLAB wrapper of SVM struct \mathrm{SVM}^{\mathrm{struct}}
Vedaldi, A · 2011
Later among the works it cites.
On the equivalence between herding and conditional gradient algorithms
Bach, F., Lacoste-Julien, S., and Obozinski, G · 2012
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Learning graph matching
Caetano, T.S., McAuley, J.J., Cheng, Li, Le, Q.V., and Smola, A.J · 2009
Cited alongside, same era.
Coresets for polytope distance
Gärtner, B. and Jaggi, M · 2009
Cited alongside, same era.
Cutting-plane training of structural SVMs
Joachims, T., Finley, T., and Yu, C · 2009
Cited alongside, same era.
Coresets, sparse greedy approximation, and the Frank-Wolfe algorithm
Clarkson, K · 2010
Cited alongside, same era.
Fast stochastic Frank-Wolfe algorithms for nonlinear SVMs
Ouyang, H. and Gray, A · 2010
Cited alongside, same era.
Bundle methods for regularized risk minimization
Teo, C.H., Vishwanathan, S.V.N., Smola, A.J., and Le, Q.V · 2010
Cited alongside, same era.
A simpler approach to obtaining an O(1/t) convergence rate for the projected stochastic subgradient method
Lacoste-Julien, S., Schmidt, M., and Bach, F · 2012
Closest in time.
Efficiency of coordinate descent methods on huge-scale optimization problems
Nesterov, Yurii · 2012
Closest in time.
Making gradient descent optimal for strongly convex stochastic optimization
Rakhlin, A., Shamir, O., and Sridharan, K · 2012
Closest in time.
Proximal stochastic dual coordinate ascent
Shalev-Shwartz, S. and Zhang, T · 2012
Closest in time.
Revisiting Frank-Wolfe: Projection-free sparse convex optimization
Jaggi, M · 2013
Closest in time.
Stochastic gradient descent for non-smooth optimization: Convergence results and optimal averaging schemes
Shamir, O. and Zhang, T · 2013
Closest in time.