We consider learning tasks where multiple target variables need to be predicted. Two approaches have been used in this setting: (a) build a separate single-target model for each ta...
Modern Bayesian Network learning algorithms are timeefficient, scalable and produce high-quality models; these algorithms feature prominently in decision support model development...
Empirical work with "Belvedere," a software environment for the construction of diagrammatic representations of evidential relations, is summarized, leading to the hypot...
Abstract. While direct, model-free reinforcement learning often performs better than model-based approaches in practice, only the latter have yet supported theoretical guarantees f...
We propose a new penalty function which, when used as regularization for empirical risk minimization procedures, leads to sparse estimators. The support of the sparse vector is ty...