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  PUBLICATIONS

 

 

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

C [1]

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. 117, Nijmegen, The Netherlands. [Proceedings download]

 

A [2]

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

 

J [3]

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]

 

A [4]

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

 

J [5]

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

 

A [6]

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

A [7]

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

 

J [8]

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

 

C [9]

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]

 

J [10]

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

 

J [11]

Constantinou, A. C., Guo, Z., and Kitson, N. K. (2023). The impact of prior knowledge on causal structure learning. Knowledge and Information Systems, Vol. 65, pp. 3385–3434. [Open-Access DOI]

 

J [12]

Okagbue, H. I., Constantinou, A. C., Iyiola, T. P., and Adedotun, A. F. (2023). Statistical analysis of regional distribution of football clubs in English top flight league. Advances and Applications in Statistics, Vol. 87, Iss.1, pp. 43–60. [Open-Access DOI]

 

   

 

2022

C [13]

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]

 

J [14]

Liu, Y., and Constantinou, A. (2022). Greedy structure learning from data that contain systematic missing values. Machine Learning, Vol. 111, pp. 38673896. [Open-Access DOI]

 

J [15]

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. 292321. [Open-Access DOI]

 

J [16]

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. 128. [Open-Access DOI]

 

J [17]

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]

 

A [18]

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

 

J [19]

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. 171193. [Open-access DOI]

   

 

2021

J [20]

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]

 

J [21]

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]

 

J [22]

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]

   

 

2020

J [23]

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]


C [24]

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]

 

J [25]

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


TR [26]

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

 

J [27]

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, 103286. [DOI]

 

J [28]

Constantinou, A. C. (2020). Learning Bayesian Networks with the Saiyan Algorithm. ACM Transactions on Knowledge Discovery from Data, Vol. 14, Iss. 4, Article 44. [DOI]

 

2019

TR [29]

Constantinou, A. (2019). The Bayesys user manual. Queen Mary University of London, London, UK. [Online]. Available: http://bayesian-ai.eecs.qmul.ac.uk/bayesys/ and http://www.bayesys.com

 
A [30]

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


ITR [31]

Constantinou, A. (2019). Rating-based Golf Tournament Simulation. Deliverable Technical Report under Collaboration NO:24.20181101.

 

 

2018

A [32] Constantinou, A., Fenton, N., & Neil, M. (2019). How do some Bayesian Network machine learned graphs compare to causal knowledge? arXiv:2101.10461 [cs.AI].

ITR [33]

Constantinou, A. (2018). As assessment of set-based ratings in capturing player ability in tennis. Deliverable Technical Report under Collaboration NO:23.20180911.


TR [34]

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


J [35]

Constantinou, A. (2018). Dolores: A model that predicts football match outcomes from all over the world. Machine Learning, pp. 1–27. [DOI]

 

Dolores ranked 2nd in the international special issue competition Machine Learning for Soccer.


J [36]

Constantinou, A., & Fenton, N. (2018). Things to know about Bayesian Networks. Significance, Vol. 15, Iss. 2, pp. 19–23. [Open Access DOI]

 

Top 20 most downloaded paper in Significance for 2017 and 2018

 

ITR [37]

Constantinou, A. (2018). Tennis player ratings based on points won and lost when serving and returning. Deliverable Technical Report under Collaboration NO:22.20180524.


J [38]

Yet, B., Neil, M., Fenton, N., Constantinou, A., & Dementiev, E. (2018). An Improved Method for Solving Hybrid Influence Diagrams. International Journal of Approximate Reasoning, Vol. 95, pp. 93–112. [DOI]


 
J [39]

Yet, B., Constantinou, A., Fenton, N., & Neil, M. (2018). Expected Value of Partial Perfect Information in Hybrid Models using Dynamic Discretization. IEEE Access, Vol. 6, pp. 7802–7817.  [DOI]


 
ITR [40] Constantinou, A. (2018). Temporal modelling and match prediction in Darts. Deliverable Technical Report under Collaboration NO:21.20171114.  
   

 

2017

 
C [41]

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 26th conference on Subjective Probability, Utility and Decision Making (SPUDM 26), Haifa, Israel, August 20-24. [long abstract, slides]


 
J [42]

Constantinou, A. C., & Fenton, N. (2017). The future of the London Buy-To-Let property market: Simulation with Temporal Bayesian Networks. PLoS ONE, 12(6): e0179297 [Open Access DOI]


 
J [43] Constantinou, A., & Fenton, N. (2017). Towards Smart-Data: Improving predictive accuracy in long-term football team performance. Knowledge-Based Systems, Vol. 124, pp 93–104. [DOI]
 
   

 

2016

 
C [44]

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 13th UAI Bayesian Modeling Applications Workshop (BMAW 2016), 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016), New York City, USA, June 25-29, 2016, pp. 54–59. [extended abstract, slides]


 
J [45]

Constantinou, A., Fenton, N., & Neil, M. (2016). Integrating expert knowledge with data in causal probabilistic networks: Preserving data-driven expectations when the expert variables remain unobserved. Expert Systems with Applications, Vol. 56, pp. 197–208. [DOI]


 
J [46]

Fenton, N., Neil, M., Lagnado, D., Marsh, W., Yet, B., & Constantinou, A. (2016). How to model mutually exclusive events based on independent causal pathways in Bayesian network models. Knowledge-Based Systems, Vol. 113, 39–50. [Open Access DOI]


 
ITR [47]

Constantinou, A. (2016). Generic Bayesian football predictions based on discrepancies in strength between adversaries. Deliverable Technical Report for Venture Sports & Events Co. Ltd under Collaboration NO:20.SPORTS-BETTING.09/05/2016.


 
J [48]

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]


J [49]

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].

 


ITR [50]

Constantinou, A. (2016). Bayesian modelling and dynamic ratings for national football team assessment: The case of EURO 2016. Deliverable Technical Report for Venture Sports & Events Co. Ltd under Collaboration NO:20.SPORTS-BETTING.09/05/2016.


 
J [51]

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].


 
C [52] Constantinou, A., & Fenton, N. (2016). Smart data – not just big data: Real-world decision making with Bayesian networks. SETforBRITAIN 2016, Engineering and Mathematical Sciences Exhibition, House of Commons, Parliament, Westminster, London, UK, March 7, 2016. [poster]

 
J [53] Coid, J. W., Ullrich S., Kallis, C., Freestone, M., Gonzalez, R., et al. (2016). Improving risk management for violence in mental health services: a multimethods approach. Programme Grants for Applied Research, Vol. 4, Iss. 16. [DOI].

ITR [54] Constantinou, A. (2016). Extending Bayesian Networks and Dynamic Rating Systems to the German, French and Spanish football leagues. Deliverable Technical Report for Venture Sports & Events Co. Ltd under Collaboration NO:19.SPORTS-BETTING.26/02/2016.

ITR [55] Constantinou, A. (2016). An expert’s guide to providing subjective inputs for Bayesian Network football models. Deliverable Technical Report for Venture Sports & Events Co. Ltd under Collaboration NO:19.SPORTS-BETTING.26/02/2016.

 
TR [56] Constantinou, A., Fenton, N., Marsh, W., & Radlinski, L. (2016). From complex questionnaires and interviewing data to intelligent Bayesian Network models. Atlas of Science, 2016. [Online, PDF].

 
ITR [57]

Constantinou, A. (2016). Algorithmic rating for determining the current level of football team performance. Deliverable Technical Report for Venture Sports & Events Co. Ltd under Collaboration NO:18.SPORTS-BETTING.17/11/2015.


 
ITR [58] Constantinou, A. (2016). Bayesian network modelling for betting decision making of the Under/Over 2.5 Goals Scored outcomes. Deliverable Technical Report for Venture Sports & Events Co. Ltd under Collaboration NO:18.SPORTS-BETTING.17/11/2015.  
   

 

2015

 
TR [59]

Fenton, N., Neil, M., & Constantinou, A. (2015). Simpson’s Paradox and the implications for medical trials. arXiv:1912.01422 [stat.ME].

 

 
J [60]

Constantinou, A., Freestone, M., Marsh, W., & Coid, J. (2015). Causal inference for violence risk management and decision support in Forensic Psychiatry. Decision Support Systems, Vol. 80, pp. 42–55. [DOI].

 

 
J [61]

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].


 
C [62]

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]


 
ITR [63]

Constantinou, A. (2015). Managing the risk of model overfitting when parameterising complex Bayesian networks with football data. Deliverable Technical Report for Venture Sports & Events Co. Ltd under Collaboration NO:18.SPORTS-BETTING.17/11/2015.


 
TR [64] Constantinou, A., Yet, B., Fenton, N., Neil, M., & Marsh, W. (2015). What is the value of missing information when assessing decisions that involve actions for intervention? Atlas of Science, 2015. [Online, PDF].

 
ITR [65] Constantinou, A. (2015). Bayesian network modelling for football match prediction of the Asian Handicap odds. Deliverable Technical Report for Venture Sports & Events Co. Ltd under Collaboration NO:17.BETTING.21/7/2015.

 
ITR [66] Constantinou, A., Yet, B., Fenton, N., & Neil, M. (2015). Bayesian Modelling Framework for Planning and Evaluating Agricultural Development Projects. Final Deliverable Report by Agena Ltd for ICRAF under Collaboration SD4/2012/214.  
   

 

2014

 
TR [67]

Coid, J. W., Ullrich, S., Kallis, C., Freestone, M., Gonzalez, R., Bui, L., Igoumenou, A., Constantinou, A., Fenton, N., Marsh, W., Yang, M., DeStavola, B., Hu, J., Shaw, J., Doyle, M., Archer-Power, L., Davoren, M., Osumili, B., McCrone, P., Barrett, K., Hindle, D., Bebbington P. (2014). Improving Risk Management in Mental Health Services – A Multi-Methods Approach. The National Institute for Health Research (NIHR), PGfAR Report, UK.


 
C [68]

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.


J [69]

Constantinou, A., Fenton, N. E., & Pollock, L. J. H. (2014). Bayesian networks for unbiased assessment of referee bias in Association Football. Psychology of Sport and Exercise, Vol. 15, Iss. 5, pp. 538–547. [DOI].

 


 
C [70]

Constantinou, A., Freestone, M., & Coid, J. W. (2014). Development of a Bayesian network for violence risk management. 14th Annual Meeting of the International Association of Forensic Mental Health Services (IAFMHS), Toronto, Canada. June 2014.


 
C [71]

Coid, J. W., Constantinou, A., Freestone, M., Kallis, C., & Bui, L. (2014). Causal models for violence risk assessment and management: a new paradigm. 14th Annual Meeting of the International Association of Forensic Mental Health Services (IAFMHS), Toronto, Canada. June 2014.


 
C [72]

Constantinou, A., Freestone, M., & Coid, J. W. (2014). Using causal inference in risk analysis of violent re-offending among UK prisoners. 15th Annual Conference of the British and Irish Group for the Study of Personality Disorder (BIGSPD), Lincoln, UK. February 2014.


 
TR [73] Constantinou, A., Fenton, N. E., & Pollock, L. J. H. (2014). Bayesian networks for unbiased assessment of referee bias in football. Football Perspectives, 4 July, 2014 [Online].
 
   

 

2013

 
J [74]

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]


TR [75]

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


 
J [76]

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].

 

 
J [77]

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].

 

 
   

 

2012 (prior and during PhD)

 
T [78]

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)].

 

 
ITR [79]

Constantinou, A. (2012). Professional business models based on football match odds. Technical Report for Agena Ltd, London, UK. August 2012.


 
J [80]

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. 322339. [DOI].

 

 
J [81]

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].

 

 
T [82]

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.


T [83] Constantinou, A. (2008). Alpha-Beta in Computational Chess. BSc Final Year Project. Developed using C#. Department of Engineering and Information Sciences, University of Hertfordshire, UK. Supervised by Prof Daniel Polani. Grade: A.
    _____________________________________________________________________  
    published online, 19/02/2012
last updated, 12/09/2024
 
       
                       

                       
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