We present a framework for efficient, accurate approximate Bayesian inference in generalized linear models (GLMs), based on the expectation propagation (EP) technique. The paramete...
Matthias Seeger, Sebastian Gerwinn, Matthias Bethg...
E-learning growth is driven by global, societal, and technological changes that both enable and require increased worker and learner mobility, access to distributed domain experti...
We formulate and study a privacy guarantee to data owners, who share information with clients by publishing views of a proprietary database. The owner identifies the sensitive pro...
In this paper we present an algorithm and software for generating arbitrarily large Bayesian Networks by tiling smaller real-world known networks. The algorithm preserves the stru...
Ioannis Tsamardinos, Alexander R. Statnikov, Laura...
Various semi-supervised learning methods have been proposed recently to solve the long-standing shortage problem of manually labeled data in sentiment classification. However, mos...