Formal languages for probabilistic modeling enable re-use, modularity, and descriptive clarity, and can foster generic inference techniques. We introduce Church, a universal langu...
Noah Goodman, Vikash K. Mansinghka, Daniel M. Roy,...
Abstract. Monitoring large distributed concurrent systems is a challenging task. In this paper we formulate (model-based) diagnosis by means of hidden state history reconstruction,...
Estimating insurance premia from data is a difficult regression problem for several reasons: the large number of variables, many of which are discrete, and the very peculiar shape...
Nicolas Chapados, Yoshua Bengio, Pascal Vincent, J...
We present a study on various statistics relevant to research on color constancy. Many of these analyses could not have been done before simply because a large database for color ...
We study the interaction between global and local techniques in data mining. Specifically, we study the collections of frequent sets in clusters produced by a probabilistic clust...