We formulate a principle for classification with the knowledge of the marginal distribution over the data points (unlabeled data). The principle is cast in terms of Tikhonov styl...
The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building large-scale multivariate stat...
This paper explores the feasibility of and challenges in developing methods for black-box monitoring of a VM's power usage at runtime, on shared virtualized compute platforms...
Bhavani Krishnan, Hrishikesh Amur, Ada Gavrilovska...
Background: Single Nucleotide Polymorphism (SNP) analysis only captures a small proportion of associated genetic variants in Genome-Wide Association Studies (GWAS) partly due to s...
Jingyuan Zhao, Simone Gupta, Mark Seielstad, Jianj...
We present BayesMD, a Bayesian Motif Discovery model with several new features. Three different types of biological a priori knowledge are built into the framework in a modular fa...