We consider the problem of learning the parameters of a Bayesian network from data, while taking into account prior knowledge about the signs of influences between variables. Such...
We present a novel approach to constraintbased causal discovery, that takes the form of straightforward logical inference, applied to a list of simple, logical statements about ca...
We introduce Cardinality Networks, a new CNF encoding of cardinality constraints. It improves upon the previously existing encodings such as the sorting networks of [ES06] in that ...
It is shown that the oorplan area minimization problem can be formulated as a convex programming problem with the numbers of variables and constraints signi cantly less than those...
We present a new probabilistic framework for finding likely variable assignments in difficult constraint satisfaction problems. Finding such assignments is key to efficient sea...
Eric I. Hsu, Matthew Kitching, Fahiem Bacchus, She...