Kahn Process Networks (KPN) are an appealing model of computation to specify streaming applications. When a KPN has to execute on a multi-processor platform, a mapping of the KPN m...
Abstract. In this paper we propose a new distance metric for probability density functions (PDF). The main advantage of this metric is that unlike the popular Kullback-Liebler (KL)...
Abstract— Shared computing utilities allocate compute, network, and storage resources to competing applications on demand. An awareness of the demands and behaviors of the hosted...
We consider approximation algorithms for buy-at-bulk network design, with the additional constraint that demand pairs be protected against edge or node failures in the network. In...
Spyridon Antonakopoulos, Chandra Chekuri, F. Bruce...
We study the computational and sample complexity of parameter and structure learning in graphical models. Our main result shows that the class of factor graphs with bounded degree...