Relational world models that can be learned from experience in stochastic domains have received significant attention recently. However, efficient planning using these models rema...
We extend tree-based methods to the prediction of structured outputs using a kernelization of the algorithm that allows one to grow trees as soon as a kernel can be defined on the...
Abstract. In this paper, we focus on the problem of preserving the privacy of sensitive relationships in graph data. We refer to the problem of inferring sensitive relationships fr...
Comparative analysis of protein networks has proven to be a powerful approach for elucidating network structure and predicting protein function and interaction. A fundamental chall...
Abstract. A probabilistic graphical model is developed in order to detect the dependent evolution between different sites in biological sequences. Given a multiple sequence alignme...