Abstract. Bayesian reinforcement learning (RL) is aimed at making more efficient use of data samples, but typically uses significantly more computation. For discrete Markov Decis...
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
Background: Microarray experiments, as well as other genomic analyses, often result in large gene sets containing up to several hundred genes. The biological significance of such ...
Jason S. M. Lee, Gurpreet Katari, Ravi Sachidanand...
The increasing popularity of high-volume performancecritical Internet applications calls for a scalable server design that allows meeting individual response-time guarantees. Cons...
— Multi-agent coordination problems can be cast as distributed optimization tasks. Probability Collectives (PCs) are techniques that deal with such problems in discrete and conti...