MapReduce has recently gained a lot of attention as a parallel programming model for scalable data-intensive business and scientific analysis. In order to benefit from this powerf...
Composite likelihood methods provide a wide spectrum of computationally efficient techniques for statistical tasks such as parameter estimation and model selection. In this paper,...
Arthur Asuncion, Qiang Liu, Alexander T. Ihler, Pa...
We formulate a risk-averse two-stage stochastic linear programming problem in which unresolved uncertainty remains after the second stage. The objective function is formulated as ...
The process of verifying a new microprocessor is a major problem for the computer industry. Currently, architects design processors to be fast, power-efficient, and reliable. Howe...
Semi-supervised learning has emerged as a popular framework for improving modeling accuracy while controlling labeling cost. Based on an extension of stochastic composite likeliho...
Joshua Dillon, Krishnakumar Balasubramanian, Guy L...