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Hybrid Conditional Gradient - Smoothing Algorithms with Applications to Sparse and Low Rank Regularization
We study a hybrid conditional gradient - smoothing algorithm (HCGS) for solving composite convex optimization problems which contain several terms over a bounded set.
On Sparsity Inducing Regularization Methods for Machine Learning
During the past years there has been an explosion of interest in learning methods based on sparsity regularization.
Sparse Prediction with the k-Support Norm
We derive a novel norm that corresponds to the tightest convex relaxation of sparsity combined with an $\ell_2$ penalty.
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