A central issue in relational learning is the choice of an appropriate bias for limiting first-order induction. The purpose of this study is to circumvent this issue within a unifo...
Bayesian network structure learning is a useful tool for elucidation of regulatory structures of biomolecular pathways. The approach however is limited by its acyclicity constraint...
S. Itani, Karen Sachs, Garry P. Nolan, M. A. Dahle...
In many machine learning problems, labeled training data is limited but unlabeled data is ample. Some of these problems have instances that can be factored into multiple views, ea...
We study a multiagent learning problem where agents can either learn via repeated interactions, or can follow the advice of a mediator who suggests possible actions to take. We pr...
Model-based Bayesian reinforcement learning has generated significant interest in the AI community as it provides an elegant solution to the optimal exploration-exploitation trade...