Several optimization techniques are hindered by uncertainties about the control flow in a program, which can generally not be determined by static methods at compile time. We pres...
This paper presents an active learning method that directly optimizes expected future error. This is in contrast to many other popular techniques that instead aim to reduce versio...
Open Answer Set Programming (OASP) is an attractive framework for integrating ontologies and rules. In general OASP is undecidable. In previous work we provided a tableau-based alg...
A new framework is presented that uses tools from duality theory of linear programming to derive graph-cut based combinatorial algorithms for approximating NP-hard classification ...
The Message Transformation Model (MTM), for modeling complex message transformation processes in data centric application scenarios, provides strong capabilities for describing the...