We study the task of randomness extraction from sources which are distributed uniformly on an unknown algebraic variety. In other words, we are interested in constructing a functi...
—In this paper we develop a distributed rate control algorithm for multiple-unicast-sessions when network coding is allowed. Building on our recent flow-based characterization o...
Abdallah Khreishah, Chih-Chun Wang, Ness B. Shroff
In this paper we propose a genetic programming approach to learning stochastic models with unsymmetrical noise distributions. Most learning algorithms try to learn from noisy data...
— We consider the problem of efficiently broadcasting incremental updates to multiple terminals that contain outdated (and possibly different) initial copies of the data. This s...
We introduce and validate bootstrap techniques to compute confidence intervals that quantify the effect of test-collection variability on average precision (AP) and mean average...