This paper studies document ranking under uncertainty. It is tackled in a general situation where the relevance predictions of individual documents have uncertainty, and are depen...
Wrapper-based feature selection is attractive because wrapper methods are able to optimize the features they select to the specific learning algorithm. Unfortunately, wrapper met...
Abstract—In this paper, we address the problem of selfadaptation in internet-scale service-oriented systems. Services need to adapt by select the best neighboring services solely...
Many applications arising in a variety of fields can be well illustrated by the task of recovering the low-rank and sparse components of a given matrix. Recently, it is discovered...
We propose a fully sequential indifference-zone selection procedure that is specifically for use within an optimization-via-simulation algorithm when simulation is costly, and par...