Dynamic Bayesian networks are structured representations of stochastic processes. Despite their structure, exact inference in DBNs is generally intractable. One approach to approx...
Generalized linear models are the most commonly used tools to describe the stimulus selectivity of sensory neurons. Here we present a Bayesian treatment of such models. Using the ...
Sebastian Gerwinn, Jakob Macke, Matthias Seeger, M...
Numerous temporal inference tasks such as fault monitoring and anomaly detection exhibit a persistence property: for example, if something breaks, it stays broken until an interve...
We study the joint feature selection problem when learning multiple related classification or regression tasks. By imposing an automatic relevance determination prior on the hypo...
Tao Xiong, Jinbo Bi, R. Bharat Rao, Vladimir Cherk...
k-anonymity is a popular measure of privacy for data publishing: It measures the risk of identity-disclosure of individuals whose personal information are released in the form of ...
Bijit Hore, Ravi Chandra Jammalamadaka, Sharad Meh...