Showing cs.LGShow all
3 papers · 1 filter
cs.LG2026
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…
cs.LG2026
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}…
cs.LG2024
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…