Large scale learning is often realistic only in a semi-supervised setting where a small set of labeled examples is available together with a large collection of unlabeled data. In...
The usability of access control mechanisms in modern distributed systems has been widely criticized but little studied. In this paper, we carefully examine one such widely deploye...
Finding bursts in data streams is attracting much attention in research community due to its broad applications. Existing burst detection methods suffer the problems that 1) the p...
We consider the problem of learning mixtures of product distributions over discrete domains in the distribution learning framework introduced by Kearns et al. [18]. We give a poly...
Power consumption is a troublesome design constraint for emergent systems such as IBM’s BlueGene /L. If current trends continue, future petaflop systems will require 100 megawat...