Recently, supervised dimensionality reduction has been gaining attention, owing to the realization that data labels are often available and indicate important underlying structure...
Using variational analysis techniques, we study convex-composite optimization problems. In connection with such a problem, we introduce several new notions as variances of the clas...
This paper studies an optimal deployment problem for a network of robotic sensors moving in the real line. Given a spatial process of interest, each individual sensor sends a pack...
Convex optimization problems arising in applications, possibly as approximations of intractable problems, are often structured and large scale. When the data are noisy, it is of i...
This paper describes a new approach to unify constraints on parameters with training data to perform parameter estimation in Bayesian networks of known structure. The method is ge...