Publications

Links to personal research websites and Google scholar publications:

Dr Anthony C. Constantinou [web][Google scholar]

Dr Neville Kenneth Kitson[Google scholar]

Dr Rendani Mbuvha [web][Google scholar]

Dr Zhigao Guo[Google scholar]

Mr Kiattikun Chobtham[Google scholar]

Mr Austin Plunkett[Google scholar]

Featured publications:

  • Chobtham, K., Constantinou, A. C., and Kitson, N. K. (2021). Hybrid Bayesian network discovery with latent variables by scoring multiple interventions. arXiv:2112.10574 [cs.LG]
  • Constantinou, A. C., Liu, Y., Kitson, N. K., Chobtham, K., and Guo, Z. (2021). Effective and efficient structure learning with pruning and model averaging strategies. arXiv:2112.00398 [cs.LG]
  • Kitson, N. K., Constantinou, A., Guo, Z., Liu, Y., and Chobtham, K. (2021). A survey of Bayesian network structure learning. arXiv:2109.11415 [cs.LG]
  • Yang, L., and Constantinou, A. (2021). Greedy structure learning from data that contains systematic missing values. arXiv:2107.04184 [cs.LG]
  • Constantinou, A., Guo, Z., and Kitson, N. K. (2021). The impact of prior knowledge on causal structure learning. arXiv:1912.00715 [cs.AI]
  • Constantinou, A. C., Liu, Y., Chobtham, K., Guo, Z., and Kitson, N. K. (2021). Large-scale empirical validation of Bayesian Network structure learning algorithms with noisy data. International Journal of Approximate Reasoning, Vol. 131, pp. 151-188. [Open-access DOI]
  • Kitson, N. K., & Constantinou, A. (2021). Learning Bayesian networks from demographic and health survey data. Journal of Biomedical Informatics, Vol. 113, Article 103588. [Open-Access DOI].
  • Constantinou, A. C. (2021). The importance of temporal information in Bayesian network structure learning. Expert Systems with Applications, Vol. 164, Article 113814. [Open-access DOI]
  • Yang, L., Constantinou, A. C., and Zhigao, G. (2020). Improving Bayesian network structure learning in the presence of measurement error. arXiv:2011.09776 [cs.AI]
  • Guo, Z. and Constantinou, A. C. (2020). Approximate learning of high dimensional Bayesian network structures via pruning of Candidate Parent Sets. Entropy, Vol. 22, Iss. 10, Article 1142. [Open-access DOI]
  • Chobtham, K. and Constantinou, A. C. (2020). Bayesian network structure learning with causal effects in the presence of latent variables. In Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM-2020), Aalborg, Denmark. [arXiv:2005.14381]
  • Constantinou, A. C. (2020). Learning Bayesian Networks that enable full propagation of evidence. IEEE Access, Vol. 8, pp. 124845-124856 [Open-Access DOI].
  • Fenton, N., Neil, M., & Constantinou, A. (2020). The Book of Why: The New Science of Cause and Effect, Judea Pearl, Dana Mackenzie, Basic Books (2018). Artificial Intelligence, Vol. 284, Article 103286. [DOI]
  • Constantinou, A. C. (2019). Evaluating structure learning algorithms with a balanced scoring function. arXiv:1905.12666[cs.LG]
  • Constantinou, A., & Fenton, N. (2018). Things to know about Bayesian Networks. Significance, Vol. 15, Iss. 2, pp. 19–23. [Open Access DOI, PDF]