5 accepted papers
Expert knowledge can greatly reduce the complexity of Bayesian network structure learning by constraining the search space. These constraints can come in the form of ancestral constraints that relate to the existence of paths between nodes. When the constraints are compiled into a directed acyclic g…
Scholars in the machine learning community have recently focused on analyzing the fairness of learning models, including clustering algorithms. In this work we study fair clustering in a probabilistic (soft) setting, where observations may belong to several clusters determined by probabilities. We i…
Markov networks are probabilistic graphical models that employ undirected graphs to depict conditional independence relationships among variables. Our focus lies in constraint-based structure learning, which entails learning the undirected graph from data through the execution of conditional indepen…
Bayesian networks represent relations between variables using a directed acyclic graph (DAG). Learning the DAG is an NP-hard problem and exact learning algorithms are feasible only for small sets of variables. We propose two scalable heuristics for learning DAGs in the linear structural equation cas…
Both learning and inference tasks on Bayesian networks are NP-hard in general. Bounded tree-width Bayesian networks have recently received a lot of attention as a way to circumvent this complexity issue; however, while inference on bounded tree-width networks is tractable, the learning problem remai…