Dr Anthony Constantinou
Senior Lecturer
Head of the MInDS Research Group
School of Electronic Engineering and Computer Science
Queen Mary University of London
Queen Mary University of London
Research
Causal machine learning, Causal discovery, Decision systems, Bayesian networks, Graphical models, Uncertainty quantification
Interests
My research focuses on causal machine learning for intelligent and autonomous decision-making under uncertainty. I collaborate with academics and industry organisations to apply my research across various disciplines including healthcare, military defence, sports, economics, and gaming. I lead the Bayesian AI lab and the broader MInDS research group.Publications of specific relevance to the Centre for Multimodal AI
2024
Using GPT-4 to guide causal machine learning.Constantinou AC, Kitson NK and Zanga A
Expert Systems With Applications, Elsevier
12-12-2024
2023
Open problems in causal structure learning: A case study of COVID-19 in the UKConstantinou A, Kitson NK, Liu Y, Chobtham K, Amirkhizi AH, Nanavati PA, Mbuvha R and Petrungaro B
Expert Systems With Applications, Elsevier vol. 234
01-08-2023
The impact of prior knowledge on causal structure learningConstantinou AC, Guo Z and Kitson NK
Knowledge and Information Systems, Springer Nature vol. 65 (8), 3385-3434.
08-04-2023
Improving the imputation of missing data with Markov Blanket discoveryLiu Y and Constantinou A
International Conference on Learning Representations (ICLR-2023).
08-02-2023
A survey of Bayesian Network structure learningKitson NK, Constantinou AC, Guo Z, Liu Y and Chobtham K
Artificial Intelligence Review, Springer
17-01-2023
2022
Effective and efficient structure learning with pruning and model averaging strategiesConstantinou AC, Liu Y, Kitson NK, Chobtham K and Guo Z
International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, Elsevier vol. 151, 292-321.
13-10-2022
Investigating the efficiency of the Asian handicap football betting market with ratings and Bayesian networksConstantinou AC
Journal of Sports Analytics vol. 8 (3), 171-193.
15-09-2022
Greedy structure learning from data that contain systematic missing values.Liu Y and Constantinou AC
Machine Learning, Springer
10-08-2022
2021
Large-scale empirical validation of Bayesian Network structure learning algorithms with noisy data.Constantinou AC, Liu Y, Chobtham K, Guo Z and Kitson NK
International Journal of Approximate Reasoning, Elsevier vol. abs/2005.09020
25-01-2021
2020
Learning Bayesian networks from demographic and health survey data.Kitson NK and Constantinou AC
Journal of Biomedical Informatics, Elsevier
17-11-2020
Approximate learning of high dimensional Bayesian network structures via pruning of Candidate Parent Sets.Guo Z and Constantinou AC
Entropy, Mdpi Ag vol. 22 (10)
10-10-2020
Bayesian network structure learning with causal effects in the presence of latent variables.Chobtham K and Constantinou AC
In Proceedings of The 10th International Conference On Probabilistic Graphical Models (Pgm-2020), Aalborg, Denmark
23-09-2020
The importance of temporal information in Bayesian network structure learningConstantinou A
Expert Systems With Applications, Elsevier vol. 164
17-08-2020
Learning Bayesian Networks That Enable Full Propagation of EvidenceConstantinou AC
Ieee Access vol. 8, 124845-124856.
08-07-2020
2018
Dolores: A model that predicts football match outcomes from all over the worldCONSTANTINOU AC
Machine Learning, Springer Verlag
03-05-2018
Things to know about Bayesian networksCONSTANTINOU AC and FENTON N
Significance, Royal Statistical Society
16-04-2018
An Improved Method for Solving Hybrid Influence DiagramsYET B, NEIL M, FENTON N, CONSTANTINOU AC and DEMENTIEV E
International Journal of Approximate Reasoning, Elsevier
01-04-2018
Expected Value of Partial Perfect Information in Hybrid Models Using Dynamic DiscretizationYet B, Constantinou A, Fenton N and Neil M
Ieee Access, Institute of Electrical and Electronics Engineers (Ieee) vol. 6, 7802-7817.
01-01-2018
2017
The future of the London Buy-To-Let property market: Simulation with temporal Bayesian NetworksCONSTANTINOU AC and Fenton N
Plos One, Public Library of Science (Plos) vol. 12 (6)
27-06-2017
2016
Improving risk management for violence in mental health services: a multimethods approachCoid JW, 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, et al.
Programme Grants For Applied Research vol. 4 (16), 1-408.
01-11-2016
How to model mutually exclusive events based on independent causal pathways in Bayesian network modelsFenton N, Neil M, Lagnado D, William M, Yet B and CONSTANTINOU AC
Knowledge-Based Systems
17-09-2016
A Bayesian Network Framework for Project Cost, Benefit and Risk Analysis with an Agricultural Development Case StudyYet B, CONSTANTINOU AC, Fenton N, Neil M, Luedeling E and Shepherd K
Expert Systems With Applications
04-05-2016
Integrating expert knowledge with data in Bayesian networks: Preserving data-driven expectations when the expert variables remain unobservedCONSTANTINOU AC, FENTON N and NEIL M
Expert Systems With Applications
18-03-2016
From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision supportConstantinou AC, Fenton N, Marsh W and Radlinski L
Artificial Intelligence in Medicine, Elsevier vol. 67, 75-93.
16-01-2016
2015
Causal inference for violence risk management and decision support in forensic psychiatryCONSTANTINOU AC, Freestone M, Marsh W and Coid J
Decision Support Systems vol. 80, 42-55.
08-10-2015
Value of information analysis for interventional and counterfactual Bayesian networks in forensic medical sciencesConstantinou AC, Yet B, Fenton N, Neil M and Marsh W
Artificial Intelligence in Medicine, Elsevier vol. 66, 41-52.
08-09-2015
Risk assessment and risk management of violent re-offending among prisonersConstantinou AC, Freestone M, Marsh W, Fenton NE and Coid J
Expert Systems With Applications vol. 42 (21)
28-05-2015
2014
Bayesian networks for unbiased assessment of referee bias in Association FootballConstantinou AC, Fenton NE and Pollock LJH
Psychology of Sport and Exercise, Elsevier vol. 15 (5), 538-547.
01-09-2014
2013
Determining the level of ability of football teams by dynamic ratings based on the relative discrepancies in scores between adversariesConstantinou A and FENTON NE
Journal of Quantitative Analysis in Sports vol. 9 (1), 37-50.
01-01-2013
Profiting from an inefficient association football gambling market: Prediction, risk and uncertainty using Bayesian networksConstantinou AC, Fenton NE and Neil M
Knowledge-Based Systems vol. 50, 60-86.
01-01-2013
2012
pi-football: A Bayesian network model for forecasting Association Football match outcomesConstantinou A, FENTON NE and Neil M
Knowledge Based Systems, Elsevier vol. 36, 322-339.
01-12-2012
Solving the problem of inadequate scoring rules for assessing probabilistic football forecasting modelsConstantinou A and FENTON NE
Journal of Quantitative Analysis in Sports vol. 8 (1)
12-03-2012
Grants
Grants of specific relevance to the Centre for Multimodal AIBayesian Artificial Intelligence for Decision Making under UncertaintyAnthony Constantinou
£502,080 EPSRC Engineering and Physical Sciences Research Council
01-06-2018 - 31-05-2021