We consider the problem of optimal allocation of computing budget to maximize the probability of correct selection in the ordinal optimization setting. This problem has been studi...
This paper presents preliminary work done on simulationbased optimization of a stochastic material-dispatching system in a retailer network. The problem we consider is one of dete...
We study an approach to policy selection for large relational Markov Decision Processes (MDPs). We consider a variant of approximate policy iteration (API) that replaces the usual...
A number of biological applications require comparison of large genome strings. Current techniques suffer from both disk I/O and computational cost because of extensive memory req...
Selective sampling, a form of active learning, reduces the cost of labeling training data by asking only for the labels of the most informative unlabeled examples. We introduce a ...