We describe a primal-dual framework for the design and analysis of online strongly convex optimization algorithms. Our framework yields the tightest known logarithmic regret bound...
views abstract groups of tasks in a workflow into high level composite tasks, in order to reuse sub-workflows and facilitate provenance analysis. However, unless a view is careful...
We propose an explicit rate indication scheme for congestion avoidance in ATM networks. In this scheme, the network switches monitor their load on each link, determining a load fac...
We design and analyze an on-line reordering buffer management algorithm with improved O log k log log k competitive ratio for non-uniform costs, where k is the buffer size. This i...
In this paper, we analyze the convergence of an iterative selftraining semi-supervised support vector machine (SVM) algorithm, which is designed for classi cation in small trainin...