This paper addresses exact learning of Bayesian network structure from data and expert's knowledge based on score functions that are decomposable. First, it describes useful ...
We propose a novel approach to encapsulate non-deterministic computations in functional logic programs. Our approach is based on set functions that return the set of all the resul...
We propose a conservative extension of HM(X), a generic constraint-based type inference framework, with bounded existential (a.k.a. abstract) and universal (a.k.a. polymorphic) da...
We describe sequential and parallel algorithms that approximately solve linear programs with no negative coefficients (a.k.a. mixed packing and covering problems). For explicitly ...
Abstract. We present the first comprehensive approach to integrating cardinality and weight rules into conflict-driven ASP solving. We begin with a uniform, constraint-based charac...
Martin Gebser, Roland Kaminski, Benjamin Kaufmann,...