We prove logarithmic regret bounds that depend on the loss L∗ T of the competitor rather than on the number T of time steps. In the general online convex optimization setting, o...
Recently proposed l1-regularized maximum-likelihood optimization methods for learning sparse Markov networks result into convex problems that can be solved optimally and efficien...
Abstract--Expertise matching, aiming to find the alignment between experts and queries, is a common problem in many real applications such as conference paper-reviewer assignment, ...
Abstract--In this paper, we deal with the problem of constrained code optimization for radar space-time adaptive processing (STAP) in the presence of colored Gaussian disturbance. ...
Antonio De Maio, Silvio De Nicola, Yongwei Huang, ...
We consider the Riemannian geometry defined on a convex set by the Hessian of a selfconcordant barrier function, and its associated geodesic curves. These provide guidance for the...