Simplification of mixture models has recently emerged as an important issue in the field of statistical learning. The heavy computational demands of using large order models dro...
We introduce a new, generic framework for private data analysis. The goal of private data analysis is to release aggregate information about a data set while protecting the privac...
We present a novel method for learning with Gaussian process regression in a hierarchical Bayesian framework. In a first step, kernel matrices on a fixed set of input points are l...
Because of name variations, an author may have multiple names and multiple authors may share the same name. Such name ambiguity affects the performance of document retrieval, web ...
In this paper, we propose a probabilistic kernel approach to preference learning based on Gaussian processes. A new likelihood function is proposed to capture the preference relat...