The low-rank matrix approximation problem involves finding of a rank k version of a m ? n matrix AAA, labeled AAAk, such that AAAk is as "close" as possible to the best ...
Randomized incremental constructions are widely used in computational geometry, but they perform very badly on large data because of their inherently random memory access patterns...
In this paper, we study a novel problem Collective Active Learning, in which we aim to select a batch set of "informative" instances from a networking data set to query ...
Many planning and design problems can be characterized as optimal search over a constrained network of conditional choices with preferences. To draw upon the advanced methods of c...
Weconsider the problemof generic broadcast in asynchronous systems with crashes, a problem that was rst studied in 12]. Roughly speaking, given a \con ict" relation on the set...