—Large scale production grids are a major case for autonomic computing. Following the classical definition of Kephart, an autonomic computing system should optimize its own beha...
Recent advances in statistical inference and machine learning close the divide between simulation and classical optimization, thereby enabling more rigorous and robust microarchit...
Adaptive Time Warp protocols in the literature are usually based on a pre-defined analytic model of the system, expressed as a closed form function that maps system state to cont...
Supervised learning deals with the inference of a distribution over an output or label space Y conditioned on points in an observation space X , given a training dataset D of pair...
While determining model complexity is an important problem in machine learning, many feature learning algorithms rely on cross-validation to choose an optimal number of features, ...