We exploit some useful properties of Gaussian process (GP) regression models for reinforcement learning in continuous state spaces and discrete time. We demonstrate how the GP mod...
In modern multimedia databases, objects can be represented by a large variety of feature representations. In order to employ all available information in a best possible way, a joi...
Hans-Peter Kriegel, Peter Kunath, Alexey Pryakhin,...
Abstract—The problem of hypothesis testing against independence for a Gauss–Markov random field (GMRF) is analyzed. Assuming an acyclic dependency graph, an expression for the...
— We study the problem of reaching a consensus in the values of a distributed system of agents with time-varying connectivity in the presence of delays. We consider a widely stud...
Pierre-Alexandre Bliman, Angelia Nedic, Asuman E. ...
: Numerical function approximation over a Boolean domain is a classical problem with wide application to data modeling tasks and various forms of learning. A great many function ap...