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...
The problem of building recommender systems has attracted considerable attention in recent years. The objective of this paper is to automatically suggest and rank a list of new it...
We investigate the problem of learning to predict moves in the board game of Go from game records of expert players. In particular, we obtain a probability distribution over legal...
Background: Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distribut...
This paper describes the Omphalos Context-Free Grammar Learning Competition held as part of the International Colloquium on Grammatical Inference 2004. The competition was created ...