Motivated by a machine learning perspective—that gametheoretic equilibria constraints should serve as guidelines for predicting agents’ strategies, we introduce maximum causal...
We show how a generic feature selection algorithm returning strongly relevant variables can be turned into a causal structure learning algorithm. We prove this under the Faithfuln...
Current methods for causal structure learning tend to be computationally intensive or intractable for large datasets. Some recent approaches have speeded up the process by first m...
Modern Bayesian Network learning algorithms are timeefficient, scalable and produce high-quality models; these algorithms feature prominently in decision support model development...
We propose a new maximum margin discriminative learning algorithm here for classification of temporal signals. It is superior to conventional HMM in the sense that it does not nee...