Abstract. We study the space complexity of randomized streaming algorithms that provide one-sided approximation guarantees; e.g., the algorithm always returns an overestimate of th...
Abstract. We present Shekoosh, a novel framework for constraint-based generation of structurally complex inputs of large sizes. Given a Java predicate that represents the desired s...
We consider the problem of recovering items matching a partially specified pattern in multidimensional trees (quad trees and k-d trees). We assume the classical model where the d...
Nicolas Broutin, Ralph Neininger, Henning Sulzbach
A general framework is proposed for gradient boosting in supervised learning problems where the loss function is defined using a kernel over the output space. It extends boosting ...
Abstract— Kinodynamic planning algorithms like RapidlyExploring Randomized Trees (RRTs) hold the promise of finding feasible trajectories for rich dynamical systems with complex...