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...
We consider the online version of the maximum vertex disjoint path problem when the underlying network is a tree. In this problem, a sequence of requests arrives in an online fash...
The paper presents a new sampling methodology for Bayesian networks that samples only a subset of variables and applies exact inference to the rest. Cutset sampling is a network s...
We describe a general technique for converting an online algorithm B to a truthtelling mechanism. We require that the original online competitive algorithm has certain "nicen...
—This paper considers the design of handoff rerouting algorithms for reducing the overall session cost in personal communication systems (PCS). Most modern communication systems ...