We present a trainable sequential-inference technique for processes with large state and observation spaces and relational structure. Our method assumes "reliable observation...
Abstract— Shared computing utilities allocate compute, network, and storage resources to competing applications on demand. An awareness of the demands and behaviors of the hosted...
Graphical models are usually learned without regard to the cost of doing inference with them. As a result, even if a good model is learned, it may perform poorly at prediction, be...
This paper gives an efficient Bayesian method for inferring the parameters of a PlackettLuce ranking model. Such models are parameterised distributions over rankings of a finite s...
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...