In this paper we consider the problem of actively learning the mean values of distributions associated with a finite number of options (arms). The algorithms can select which opti...
We present a framework for efficient, accurate approximate Bayesian inference in generalized linear models (GLMs), based on the expectation propagation (EP) technique. The paramete...
Matthias Seeger, Sebastian Gerwinn, Matthias Bethg...
We evaluate the performance of quantum arithmetic algorithms run on a distributed quantum computer (a quantum multicomputer). We vary the node capacity and I/O capabilities, and t...
Rodney Van Meter, Kae Nemoto, W. J. Munro, Kohei M...
We present a model for the parallel performance of algorithms that consist of concurrent, two-dimensional wavefronts implemented in a message passing environment. The model combine...
Adolfy Hoisie, Olaf M. Lubeck, Harvey J. Wasserman
stractions underlying distributed computing. We attempted to keep our preaims at an abstract and general level. In this column, we make those claims more concrete. More precisely, ...