In this paper we present a family of models and learning algorithms that can simultaneously align and cluster sets of multidimensional curves measured on a discrete time grid. Our...
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. Dete...
Background: Inferring gene regulatory networks from data requires the development of algorithms devoted to structure extraction. When only static data are available, gene interact...
The proliferation of text documents on the web as well as within institutions necessitates their convenient organization to enable efficient retrieval of information. Although tex...
Sriharsha Veeramachaneni, Diego Sona, Paolo Avesan...
In this paper, we introduce a simple but efficient greedy algorithm, called SINCO, for the Sparse INverse COvariance selection problem, which is equivalent to learning a sparse Ga...