167 citations · 522 across the 16 of their papers we have counts for
19 papers
Fast Learning of Optimal Policy Trees
James Cussens, Julia Hatamyar, Vishalie Shah +1
We develop and implement a version of the popular "policytree" method (Athey and Wager, 2021) using discrete optimisation techniques. We test the performance of our algorithm in fi…
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…
Kernel-based Approach to Handle Mixed Data for Inferring Causal Graphs
Teny Handhayani, James Cussens
Causal learning is a beneficial approach to analyze the cause and effect relationships among variables in a dataset. A causal graph can be generated from a dataset using a particul…
On Pruning for Score-Based Bayesian Network Structure Learning
Alvaro H. C. Correia, James Cussens, Cassio de Campos
Many algorithms for score-based Bayesian network structure learning (BNSL), in particular exact ones, take as input a collection of potentially optimal parent sets for each variabl…
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…