We propose a highly efficient framework for penalized likelihood kernel methods applied to multiclass models with a large, structured set of classes. As opposed to many previous a...
Communication across the science-policy interface is complicated by uncertainty and ignorance associated with predictions on which to base policies. The international symposium â€...
Traditional information retrieval models assume that users express their information needs via text queries (i.e., their "talk"). In this poster, we consider Web browsin...
Mikhail Bilenko, Ryen W. White, Matthew Richardson...
We present a biologically inspired approach to the dynamic assignment and reassignment of a homogeneous swarm of robots to multiple locations, which is relevant to applications lik...
Abstract--We present an algorithm that coevolves fitness predictors, optimized for the solution population, which reduce fitness evaluation cost and frequency, while maintaining ev...