167 citations · 522 across the 16 of their papers we have counts for
13 papers · 1 filter
Learning All Credible Bayesian Network Structures for Model Averaging
Zhenyu A. Liao, Charupriya Sharma, James Cussens +1
A Bayesian network is a widely used probabilistic graphical model with applications in knowledge discovery and prediction. Learning a Bayesian network (BN) from data can be cast as…
Online Causal Structure Learning in the Presence of Latent Variables
Durdane Kocacoban, James Cussens
We present two online causal structure learning algorithms which can track changes in a causal structure and process data in a dynamic real-time manner. Standard causal structure l…
Finding All Bayesian Network Structures within a Factor of Optimal
Zhenyu A. Liao, Charupriya Sharma, James Cussens +1
A Bayesian network is a widely used probabilistic graphical model with applications in knowledge discovery and prediction. Learning a Bayesian network (BN) from data can be cast as…
Finding Minimal Cost Herbrand Models with Branch-Cut-and-Price
James Cussens
Given (1) a set of clauses in some first-order language and (2) a cost function , mapping each ground atom in the Herbrand b…
Bayesian Network Structure Learning with Integer Programming: Polytopes, Facets, and Complexity
James Cussens, Matti Järvisalo, Janne H. Korhonen +1
The challenging task of learning structures of probabilistic graphical models is an important problem within modern AI research. Recent years have witnessed several major algorithm…
First-order integer programming for MAP problems
James Cussens
Finding the most probable (MAP) model in SRL frameworks such as Markov logic and Problog can, in principle, be solved by encoding the problem as a `grounded-out' mixed integer prog…