In this work a new adaptive fast variational sparse Bayesian learning (V-SBL) algorithm is proposed that is a variational counterpart of the fast marginal likelihood maximization ...
Dmitriy Shutin, Thomas Buchgraber, Sanjeev R. Kulk...
Abstract. To generalize the Fisher Discriminant Analysis (FDA) algorithm to the case of discriminant functions belonging to a nonlinear, finite dimensional function space F (Nonli...
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...
: We present the use of mapping functions to automatically generate levels of detail with known error bounds for polygonal models. We develop a piece-wise linear mapping function f...
We propose a programming paradigm called compress-and-conquer (CC) that leads to optimal performance on multicore platforms. Given a multicore system of p cores and a problem of s...