36 citations · 66 across the 8 of their papers we have counts for
8 papers
Hybrid Conditional Gradient - Smoothing Algorithms with Applications to Sparse and Low Rank Regularization
Andreas Argyriou, Marco Signoretto, Johan Suykens
We study a hybrid conditional gradient - smoothing algorithm (HCGS) for solving composite convex optimization problems which contain several terms over a bounded set. Examples of t…
On Sparsity Inducing Regularization Methods for Machine Learning
Andreas Argyriou, Luca Baldassarre, Charles A. Micchelli +1
During the past years there has been an explosion of interest in learning methods based on sparsity regularization. In this paper, we discuss a general class of such methods, in wh…
PRISMA: PRoximal Iterative SMoothing Algorithm
Francesco Orabona, Andreas Argyriou, Nathan Srebro
Motivated by learning problems including max-norm regularized matrix completion and clustering, robust PCA and sparse inverse covariance selection, we propose a novel optimization…
Sparse Prediction with the -Support Norm
Andreas Argyriou, Rina Foygel, Nathan Srebro
We derive a novel norm that corresponds to the tightest convex relaxation of sparsity combined with an penalty. We show that this new {\em -support norm} provides a tig…
A Regularization Approach for Prediction of Edges and Node Features in Dynamic Graphs
Emile Richard, Andreas Argyriou, Theodoros Evgeniou +1
We consider the two problems of predicting links in a dynamic graph sequence and predicting functions defined at each node of the graph. In many applications, the solution of one p…
A General Framework for Structured Sparsity via Proximal Optimization
Andreas Argyriou, Luca Baldassarre, Jean Morales +1
We study a generalized framework for structured sparsity. It extends the well-known methods of Lasso and Group Lasso by incorporating additional constraints on the variables as par…