In the context of binary classification, we define disagreement as a measure of how often two independently-trained models differ in their classification of unlabeled data. We exp...
Bayesian learning in undirected graphical models--computing posterior distributions over parameters and predictive quantities-is exceptionally difficult. We conjecture that for ge...
A fundamental problem in neural network research, as well as in many other disciplines, is finding a suitable representation of multivariate data, i.e. random vectors. For reasons...
A large number of database index structures have been proposed over the last two decades, and little consensus has emerged regarding their relative e ectiveness. In order to empir...
Joseph M. Hellerstein, Lisa Hellerstein, George Ko...
We consider online routing algorithms for routing between the vertices of embedded planar straight line graphs. Our results include (1) two deterministic memoryless routing algorit...