The hierarchical Dirichlet process (HDP) is a Bayesian nonparametric mixed membership model--each data point is modeled with a collection of components of different proportions. T...
Sinead Williamson, Chong Wang, Katherine A. Heller...
Determinantal point processes (DPPs), which arise in random matrix theory and quantum physics, are natural models for subset selection problems where diversity is preferred. Among...
Abstract. We formulate the problem of least squares temporal difference learning (LSTD) in the framework of least squares SVM (LS-SVM). To cope with the large amount (and possible ...
In this paper we construct an atlas that captures functional characteristics of a cognitive process from a population of individuals. The functional connectivity is encoded in a lo...
Georg Langs, Danial Lashkari, Andrew Sweet, Yanmei...
A central problem in learning is selection of an appropriate model. This is typically done by estimating the unknown generalization errors of a set of models to be selected from a...