8 citations · 18 across the 7 of their papers we have counts for
7 papers · 1 filter
Tuning structure learning algorithms with out-of-sample and resampling strategies
Kiattikun Chobtham, Anthony C. Constantinou
One of the challenges practitioners face when applying structure learning algorithms to their data involves determining a set of hyperparameters; otherwise, a set of hyperparameter…
Open problems in causal structure learning: A case study of COVID-19 in the UK
Anthony Constantinou, Neville K. Kitson, Yang Liu +5
Causal machine learning (ML) algorithms recover graphical structures that tell us something about cause-and-effect relationships. The causal representation praovided by these algor…
Parallel Sampling for Efficient High-dimensional Bayesian Network Structure Learning
Zhigao Guo, Anthony C. Constantinou
Score-based algorithms that learn the structure of Bayesian networks can be used for both exact and approximate solutions. While approximate learning scales better with the number…
Bayesian network structure learning with causal effects in the presence of latent variables
Kiattikun Chobtham, Anthony C. Constantinou
Latent variables may lead to spurious relationships that can be misinterpreted as causal relationships. In Bayesian Networks (BNs), this challenge is known as learning under causal…
Large-scale empirical validation of Bayesian Network structure learning algorithms with noisy data
Anthony C. Constantinou, Yang Liu, Kiattikun Chobtham +2
Numerous Bayesian Network (BN) structure learning algorithms have been proposed in the literature over the past few decades. Each publication makes an empirical or theoretical case…
Learning Bayesian Networks that enable full propagation of evidence
Anthony Constantinou
This paper builds on recent developments in Bayesian network (BN) structure learning under the controversial assumption that the input variables are dependent. This assumption can…