167 citations · 522 across the 16 of their papers we have counts for
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cs.LG2019★ 1 cited
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…
stat.ML2019★ 5 cited
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…
cs.AI2019
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…