While most supervised machine learning models assume that training examples are sampled at random or adversarially, this article is concerned with models of learning from a cooper...
Sandra Zilles, Steffen Lange, Robert Holte, Martin...
We propose a fully Bayesian methodology for generalized kernel mixed models (GKMMs), which are extensions of generalized linear mixed models in the feature space induced by a repr...
Following Hartigan (1975), a cluster is defined as a connected component of the t-level set of the underlying density, that is, the set of points for which the density is greater...
We present a class of graphical models for directly representing the joint cumulative distribution function (CDF) of many random variables, called cumulative distribution networks...
This paper1 presents novel algorithms and applications for a particular class of mixed-norm regularization based Multiple Kernel Learning (MKL) formulations. The formulations assu...
Jonathan Aflalo, Aharon Ben-Tal, Chiranjib Bhattac...