4 papers
Learning Bayesian and Markov Networks with an Unreliable Oracle
Juha Harviainen, Pekka Parviainen, Vidya Sagar Sharma
We study constraint-based structure learning of Markov networks and Bayesian networks in the presence of an unreliable conditional independence oracle that makes at most a bounded…
Evaluating Prediction Uncertainty Estimates from BatchEnsemble
Morten Blørstad, Herman Jangsett Mostein, Nello Blaser +1
Deep learning models struggle with uncertainty estimation. Many approaches are either computationally infeasible or underestimate uncertainty. We investigate \textit{BatchEnsemble}…
Graph Reconstruction with a Connected Components Oracle
Juha Harviainen, Pekka Parviainen
In the Graph Reconstruction (GR) problem, the goal is to recover a hidden graph by utilizing some oracle that provides limited access to the structure of the graph. The interest is…
Structural perspective on constraint-based learning of Markov networks
Tuukka Korhonen, Fedor V. Fomin, Pekka Parviainen
Markov networks are probabilistic graphical models that employ undirected graphs to depict conditional independence relationships among variables. Our focus lies in constraint-base…