We consider a fundamental problem in computational learning theory: learning an arbitrary Boolean function which depends on an unknown set of k out of n Boolean variables. We give...
Elchanan Mossel, Ryan O'Donnell, Rocco A. Servedio
We propose a model for deterministic distributed function computation by a network of identical and anonymous nodes. In this model, each node has bounded computation and storage c...
Julien M. Hendrickx, Alexander Olshevsky, John N. ...
For years, the computation rate of processors has been much faster than the access rate of memory banks, and this divergence in speeds has been constantly increasing in recent yea...
Guy E. Blelloch, Phillip B. Gibbons, Yossi Matias,...
Bayesian networks, equivalently graphical Markov models determined by acyclic digraphs or ADGs (also called directed acyclic graphs or dags), have proved to be both effective and ...
Many of today's complex computer applications are being modeled and constructed using the principles inherent to real-time distributed object systems. In response to this dem...