Publications are sorted by year as Journal ( J ) or arXiv ( A ), Conference ( C ), Academic Technical Report ( TR ), Industry Technical Report ( ITR ), or Thesis ( T ).

2024

Kitson, N. K., and Constantinou, A. (2024). Eliminating Variable Order Instability in Greedy Score-Based Structure Learning. In Proceedings of the 12th Conference on Probabilistic Graphical Models (PGM-2024), Proceedings of Machine Learning Research (PMLR), Vol. 246, pp. 1–17, Nijmegen, The Netherlands. [Proceedings download]

Constantinou, A. , Kitson, N. K, and Zanga, A. (2024). Using GPT-4 to guide causal machine learning. arXiv:2407.18607 [cs.AI]

Kitson, N. K., and Constantinou, A. (2024). The impact of variable ordering on Bayesian network structure learning. Data Mining and Knowledge Discovery, Vol. 38, pp. 2545-2569. [Open-Access DOI]

Petrungaro, B., Kitson, N. K., and Constantinou, A. (2024). Investigating potential causes of Sepsis with Bayesian network structure learning. arXiv:2406.09207 [cs.LG]

Chobtham, K., and Constantinou, A. (2024). Tuning structure learning algorithms with out-of-sample and resampling strategies. Knowledge and Information Systems, Vol. 66, pp. 4927-4955. [Open-Access DOI] 

Zahoor, S., Constantinou, A., Curtins, T. M., and Hasanuzzaman, M. (2024). Investigating the validity of structure learning algorithms in identifying risk factors for intervention in patients with diabetes. arXiv:2403.14327

2023

Kitson, N. K., and Constantinou, A. (2023). Causal discovery using dynamically requested knowledge. arXiv:2310.11154

Constantinou, A., Kitson N. K., Liu, Y., Chobtham, K., Hashemzadeh, A., Nanavati, P. A., Mbuvha, R., and Petrungaro, B. (2023). Open problems in causal structure learning: A case study of COVID-19 in the UK. Expert Systems with Applications, Vol. 234, Article 121069 [Open-Access DOI] 

Liu, Y., and Constantinou, A. (2023). Improving the imputation of missing data with Markov Blanket discovery. In Proceedings of the 11th International Conference on Learning Representations (ICLR-2023), Kigali, Rwanda. [Proceedings download]

Kitson, N. K., Constantinou, A., Guo, Z., Liu, Y., and Chobtham, K. (2023). A survey of Bayesian network structure learning. Artificial Intelligence Review, Vol. 56, pp. 8721–8814. [Open-Access DOI] 

2022

Chobtham, K., and Constantinou, A. (2022). Discovery and density estimation of latent confounders in Bayesian networks with evidence lower bound. In Proceedings of the 11th International Conference on Probabilistic Graphical Models (PGM-2022), Almeria, Spain, Oct 2022. [PMLR Proceedings download]

Liu, Y., and Constantinou, A. (2022). Greedy structure learning from data that contain systematic missing values. Machine Learning, Vol. 111, pp. 3867–3896. [Open-Access DOI]

Constantinou, A., Liu, Y., Kitson, N. K., Chobtham, K., and Guo, Z. (2022). Effective and efficient structure learning with pruning and model averaging strategies. International Journal of Approximate Reasoning, Vol. 151, pp. 292–321. [Open-Access DOI]

Liu, Y., Constantinou, A., and Guo, Z. (2022). Improving Bayesian network structure learning in the presence of measurement error. Journal of Machine Learning Research, Vol. 23, Iss. 324, pp. 1–28. [Open-Access DOI]

Chobtham, K., Constantinou, A., and Kitson, N. K. (2022). Hybrid Bayesian network discovery with latent variables by scoring multiple interventions. Data Mining and Knowledge Discovery, Vol. 37, pp. 476-520. [Open-Access DOI]

Guo, Z. and Constantinou, A. C. (2022). Parallel Sampling for efficient high-dimensional Bayesian network structure learning. arXiv:2202.09691 [cs.LG]

Constantinou, A. (2022). Investigating the efficiency of the Asian handicap football betting market with ratings and Bayesian networks. Journal of Sports Analytics, Vol. 8, pp. 171–193. [Open-access DOI]

2021

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]

2020

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. [PMLR Proceedings download]

Constantinou, A. C. (2020). Learning Bayesian Networks that enable full propagation of evidence. IEEE Access, Vol. 8, pp. 124845-123856. [Open-Access DOI]

Constantinou, A. C., Liu, Y., Chobtham, K., Guo, Z., and Kitson, N. K. (2020). The Bayesys data and Bayesian network repository. Queen Mary University of London, London, UK. [Online]. Available: http://bayesian-ai.eecs.qmul.ac.uk/bayesys/ and http://www.bayesys.com

2019

Constantinou, A. (2019). Evaluating structure learning algorithms with a balanced scoring function. arXiv:1905.12666 [cs.LG].

2018

Constantinou, A. (2018). Bayesian Artificial Intelligence for Decision Making under Uncertainty. Engineering and Physical Sciences Research Council, EP/S001646/1. [PDF]

Fenton, N., Constantinou, A., & Neil, M. (2017). Combining judgments with messy data to build Bayesian Network models for improved intelligence analysis and decision support. In Proceedings of the 26^th conference on Subjective Probability, Utility and Decision Making (SPUDM 26), Haifa, Israel, August 20-24. [long abstract, slides]

Constantinou, A., & Fenton, N. (2016). Improving predictive accuracy using Smart-Data rather than Big-Data: A case study of soccer teams’ evolving performance. In Proceedings of the 13^th UAI Bayesian Modeling Applications Workshop (BMAW 2016), 32^nd Conference on Uncertainty in Artificial Intelligence (UAI 2016), New York City, USA, June 25-29, 2016, pp. 54–59. [extended abstract, slides]

Constantinou, A., Fenton, N., Marsh, W. & Radlinski, L. (2016). From complex questionnaire and interviewing data to intelligent Bayesian models for medical decision support. Artificial Intelligence in Medicine, Vol. 60, pp. 75–93. [DOI]

Yet, B., Constantinou, A., Fenton, N., Neil, M., Luedeling, E., & Shepherd, K. (2016). A Bayesian Network Framework for Project Cost, Benefit and Risk Analysis with an Agricultural Development Case Study. Expert Systems with Applications, Vol. 60, 141–155. [DOI].

Constantinou, A., Yet, B., Fenton, N., Neil, M., & Marsh, W. (2016). Value of Information analysis for Interventional and Counterfactual Bayesian networks in Forensic Medical Sciences.  Artificial Intelligence in Medicine, Vol. 66, pp. 41–52. [DOI].

Constantinou, A., Freestone, M. F., Marsh, W., Coid, J., & Fenton, N. (2015). Risk assessment and risk management of violent reoffending among prisoners. Expert Systems with Applications, Vol. 42, Iss. 21, pp. 7511–7529. [DOI].

Yet, B., Constantinou, A., Fenton, N., Neil, M., Luedeling, E., & Shepherd, K. (2015). Project Cost, Benefit and Risk Analysis using Bayesian Networks. In Proceedings of the 12th UAI Bayesian Modeling Applications Workshop, 31st Conference on Uncertainty in Artificial Intelligence (UAI 2015), Amsterdam, Netherlands, July 12-16, 2015. [Abstract]

Marsh, W., Constantinou, A., Yet, B., & Fenton, N. (2014). Evidence synthesis for patient-specific decision support using Bayesian networks. Life Sciences Conference: Population Health in a Post-Genomic Era, London, UK, December 2014.

Constantinou, A., & Fenton, N. E. (2013). Profiting from arbitrage and odds biases of the European gambling market. The Journal of Gambling Business and Economics, Vol. 7, Iss. 2, pp. 41–70. [PDF]

Constantinou, A. (2013). Football: Win, Lose or Draw? Computer Science For Fun (CS4FN) [Online].

Constantinou, A., Fenton, N. E., & Neil, M. (2013). Profiting from an Inefficient Association Football Gambling Market: Prediction, Risk and Uncertainty Using Bayesian Networks. Knowledge-Based Systems, Vol. 50, pp. 60–86. [Open Access DOI].

Constantinou, A., & Fenton, N. E. (2013). Determining the level of ability of football teams by dynamic ratings based on the relative discrepancies in scores between adversaries. Journal of Quantitative Analysis in Sports, Vol. 9, Iss. 1, pp. 37–50. [DOI].

Constantinou, A. (2012). Bayesian Networks for Prediction, Risk Assessment and Decision Making in an inefficient Association Football gambling market. Ph.D Thesis, Risk & Information Management Research Group, School of Electronic Engineering and Computer Science, Queen Mary, University of London. Primary Supervision: Prof. Norman Fenton, Secondary Supervision: Prof. Martin Neil. September 2012. [Original version] [Restructured version (easier to read)].

Constantinou, A., Fenton, N. E., & Neil, M. (2012). pi-football: A Bayesian network model for forecasting Association Football match outcomes. Knowledge-Based Systems, Vol. 36, pp. 322–339. [DOI].

Constantinou, A., & Fenton, N. E. (2012). Solving the Problem of Inadequate Scoring Rules for Assessing Probabilistic Football Forecast Models. Journal of Quantitative Analysis in Sports, Vol. 8, Iss. 1, Article 1. [DOI].

Constantinou, A. (2009). Mathematical study of rational behaviour in Poker. MSc Thesis. Developed using C++. Department of Engineering and Information Sciences, University of Hertfordshire, UK, Supervised by Prof Daniel Polani. Grade: A.