The increasing number of knowledge-based systems that build on a Bayesian belief network or influence diagram acknowledge the usefulness of these frameworks for addressing complex...
This work explores issues of computational disclosure control. We examine assumptions in the foundations of traditional problem statements and abstract models. We offer a comprehe...
Rick Crawford, Matt Bishop, Bhume Bhumiratana, Lis...
— Despite significant efforts to obtain an accurate picture of the Internet’s connectivity structure at the level of individual autonomous systems (ASes), much has remained un...
Ricardo V. Oliveira, Dan Pei, Walter Willinger, Be...
We present a new approximate inference algorithm for Deep Boltzmann Machines (DBM's), a generative model with many layers of hidden variables. The algorithm learns a separate...
We present a unified framework for reasoning about worst-case regret bounds for learning algorithms. This framework is based on the theory of duality of convex functions. It brin...