Learning Bayesian networks from data is an N-P hard problem with important practical applications. Several researchers have designed algorithms to overcome the computational comple...
We use techniques from sample-complexity in machine learning to reduce problems of incentive-compatible mechanism design to standard algorithmic questions, for a broad class of re...
Maria-Florina Balcan, Avrim Blum, Jason D. Hartlin...
We consider the classical rumor spreading problem, where a piece of information must be disseminated from a single node to all n nodes of a given network. We devise two simple pus...
George Giakkoupis, Thomas Sauerwald, He Sun, Phili...
Navigational queries on Web-accessible life science sources pose unique query optimization challenges. The objects in these sources are interconnected to objects in other sources, ...
Jens Bleiholder, Samir Khuller, Felix Naumann, Lou...
This paper introduces hyper-ellipsoids as an improvement to hyper-spheres as intrusion detectors in a negative selection problem within an artificial immune system. Since hyper-s...
Joseph M. Shapiro, Gary B. Lamont, Gilbert L. Pete...