We study the problem of learning a kernel which minimizes a regularization error functional such as that used in regularization networks or support vector machines. We consider thi...
Andreas Argyriou, Charles A. Micchelli, Massimilia...
—We describe and evaluate a suite of distributed and computationally efficient algorithms for solving a class of convex optimization problems in wireless sensor networks. The pr...
Abstract. In systems biology, the number of models of cellular processes increases rapidly, but re-using models in different contexts or for different questions remains a challengi...
—We present an optimal methodology for dynamic voltage scheduling problem in the presence of realistic assumption such as leakage-power and intra-task overheads. Our contribution...
We consider a rather general class of mathematical programming problems with data uncertainty, where the uncertainty set is represented by a system of convex inequalities. We prove...