We consider the problem of learning Gaussian multiresolution (MR) models in which data are only available at the finest scale and the coarser, hidden variables serve both to captu...
Myung Jin Choi, Venkat Chandrasekaran, Alan S. Wil...
Many statistical M-estimators are based on convex optimization problems formed by the weighted sum of a loss function with a norm-based regularizer. We analyze the convergence rat...
Alekh Agarwal, Sahand Negahban, Martin J. Wainwrig...
Background: The identification of biologically interesting genes in a temporal expression profiling dataset is challenging and complicated by high levels of experimental noise. Mo...
A notable gap in research on statistical dependency parsing is a proper conditional probability distribution over nonprojective dependency trees for a given sentence. We exploit t...
Abstract. Markov and Conditional random fields (CRFs) used in computer vision typically model only local interactions between variables, as this is computationally tractable. In t...