Learning from ambiguous training data is highly relevant in many applications. We present a new learning algorithm for classification problems where labels are associated with se...
Constraint programming is a powerful paradigm that offers many different strategies for solving problems. Choosing a good strategy is difficult; choosing a poor strategy wastes r...
Cormac Gebruers, Brahim Hnich, Derek G. Bridge, Eu...
We study how several collective operations like broadcast, reduction, scan, etc. can be composed efficiently in complex parallel programs. Our specific contributions are: (1) a fo...
Sergei Gorlatch, Christoph Wedler, Christian Lenga...
This paper examines some of the reporting and research practices concerning empirical work in genetic programming. We describe several common loopholes and offer three case studie...
Jason M. Daida, Derrick S. Ampy, Michael Ratanasav...
We present probabilistic logic programming under inheritance with overriding. This approach is based on new notions of entailment for reasoning with conditional constraints, which...