Publications using GOBNILP (including comparisons of GOBNILP to other approaches)

• Subhadeep Karan, Zainul Abideen Sayed and Jaroslaw Zola, End-to-End Bayesian Networks Exact Learning in Shared Memory IEEE Transactions on Parallel and Distributed Systems, Vol. 35, No. 4, 634-645, April 2024.
• Victor-Alexandru Darvariu, Stephen Hailes and Mirco Musolesi, Tree Search in DAG Space with Model-based Reinforcement Learning for Causal Discovery , arXiv:2310.13576, 2024.
• Janusz Rusek, Umberto Alibrandi, Leszek Słowik and Leszek Chomacki, BNSL GOBNILP algorithm in application to damage intensity prognostic system to RC multistorey residential buildings subjected to negative impacts of the industrial environment of mines. Journal of Building Engineering 80, 107885, 2023.
• Zuowu Zheng, Chao Wang, Xiaofeng Gao and Guihai Chen RBNets: A Reinforcement Learning Approach for Learning Bayesian Network Structure. Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2023. Lecture Notes in Computer Science, vol 14171, 2023.
• Kefei Yan, Wei Fang, Hengyang Lu, Xin Zhang, Jun Sun and Xiaojun Wu, Mutual Information-Guided GA for Bayesian Network Structure Learning IEEE Transactions on Knowledge And Data Engineering, 8282-8299, vol. 35, no. 8, 2023
• James Cussens, Branch-Price-and-Cut for Causal Discovery Proc. 2nd Conference on Causal Learning and Reasoning (CLeaR 2023), PMLR, 2023.
• Charupriya Sharma, Improved Scalability and Accuracy of Bayesian Network Structure Learning in the Score-and-Search Paradigm. PhD Thesis, University of Waterloo, May 2023.
• Sharma, N. and Millstein, J. CausNet: generational orderings based search for optimal Bayesian networks via dynamic programming with parent set constraints. BMC Bioinformatics 24, 46 (2023)
• Jiří Vomlel, Václav Kratochvíl, František Kratochvíl, Structural learning of mixed noisy-OR Bayesian networks . International Journal of Approximate Reasoning, 161 (2023) 108990.
• GenoMed4All consortium, A sex-informed approach to improve the personalised decision making process in myelodysplastic syndromes: a multicentre, observational cohort study . Lancet Haemotology, 10: e117–28, 2022.
• Chase Yakaboski and Eugene Santos, Jr. Learning the Finer Things: Bayesian Structure Learning at the Instantiation Level, Proc. AAAI-23, March 2023.
• Yang Yang. Generalized Bayesian Network Classifiers, MSc Thesis, Eindhoven University of Technology, 2022.
• Yang Liu, Anthony Constantinou and Zhigao Guo. Improving Bayesian Network Structure Learning in the Presence of Measurement Error, JMLR 23:(324):1-28, 2022.
• Noa Ben-David and Sivan Sabato. Active Structure Learning of Bayesian Networks in an Observational Setting, JMLR 23:(188):1-38, 2022.
• K. Yan, W. Fang, H. Lu, X. Zhang, J. Sun and X. Wu, Mutual Information-Guided GA for Bayesian Network Structure Learning, IEEE Transactions on Knowledge and Data Engineering, 2022.
• Xiangyuan Tan, Xiaoguang Gao, Zidong Wang, Hao Han, Xiaohan Liu and Daqing Chen. Learning the structure of Bayesian networks with ancestral and/or heuristic partition. Information Sciences, Volume 584, 2022, Pages 719-751.
• Charupriya Sharma and Peter van Beek. Scalable Bayesian Network Structure Learning with Splines, arXiv 2110.14626, 2022.
• Anthony C. Constantinou, Yang Liu, Neville K. Kitson, Kiattikun Chobtham, and Zhigao Guo. Effective and efficient structure learning with pruning and model averaging strategies, arXiv 2112.00398, 2022.
• Qingwang Zhang, Sihang Liu, Ruihong Xu, Zemeng Yang & Jianxiao Liu. KTOBS: An Approach of Bayesian Network Learning Based on K-tree Optimizing Ordering-Based Search Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2021
• Xuan Yang and Chen Yang and Jimeng Lei and Jianxiao Liu. An Approach of Epistasis Detection Using Integer Linear Programming Optimizing Bayesian Network EEE/ACM Transactions on Computational Biology and Bioinformatics, doi: 10.1109/TCBB.2021.3092719, 2021.
• Jorge Díez García-Victoria Score-based Bayesian networks for the discovery of effective connectivity in fMRI data with the use of the Balloon model, Master thesis, E.T.S. de Ingenieros Informáticos (UPM), 2021.
• Haoyue Dai, Rui Ding, Yuanyuan Jiang, Shi Han and Dongmei Zhang. ML4C: Seeing Causality Through Latent Vicinity. Arxiv 2110.00637, Oct 2021.
• Rui Chen, Sanjeeb Dash and Tian Gao. Integer Programming for Causal Structure Learning in the Presence of Latent Variables Proceedings of the 38th International Conference on Machine Learning (ICML21), PMLR 139:1550-1560, 2021.
• Liu, X., Gao, X., Wang, Z. and Ru, X. Improved Local Search with Momentum for Bayesian Networks Structure Learning. Entropy 2021, 23, 750.
• Nicos Angelopoulos, Aikaterini Chatzipli, Jyoti Nangalia, Francesco Maura and Peter J. Campbell, Bayesian networks elucidate complex genomic landscapes in cancer. Commun Biol 5, 306 (2022).
• Goutham Rajendran, Bohdan Kivva, Ming Gao, Bryon Aragam. Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families. NeurIPS 2021
• Kari Rantanen, Antti Hyttinen, Matti Järvisalo. Maximal ancestral graph structure learning via exact search. Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1237-1247, 2021.
• Fulya Trösser, Simon de Givry and George Katsirelos. Improved Acyclicity Reasoning for Bayesian Network Structure Learning with Constraint Programming, pp 4250-4257, Proc IJCAI-21, August 2021
• Felix L. Rios, Giusi Moffa and Jack Kuipers. Benchpress: a scalable and platform-independent workflow for benchmarking structure learning algorithms for graphical models, arXiv:2107.03863, July 2021
• X. Yang, C. Yang, J. Lei and J. Liu. An Approach of Epistasis Detection Using Integer Linear Programming Optimizing Bayesian Network, IEEE/ACM Transactions on Computational Biology and Bioinformatics, June 2021.
• Noa Ben-David and Sivan Sabato. Active Structure Learning of Bayesian Networks in an Observational Setting , arXiv:2103.13796, March 2021
• Yue Yu, Tian Gao, Naiyu Yin, Qiang Ji. DAGs with No Curl: An Efficient DAG Structure Learning Approach, Proceedings of the 38th International Conference on Machine Learning, PMLR 139:12156-12166, 2021.
• Anthony C. Constantinou, Yang Liua, Kiattikun Chobthama, Zhigao Guoa, Neville K. Kitson. Large-scale empirical validation of Bayesian Network structure learning algorithms with noisy data, International Journal of Approximate Reasoning 131 (2021) 151–188
• A. C. Constantinou. Learning Bayesian Networks That Enable Full Propagation of Evidence, IEEE Access, vol. 8, pp. 124845-124856, July 2020.
• Jussi Viinikka, Johan Pensar, Antti Hyttinen and Mikko Koivisto. Towards Scalable Bayesian Learning of Causal DAGs, pp. 6584-6594, Proc. NeurIPS 2020
• Pierre Gillot and Pekka Parviainen. Scalable Bayesian Network Structure Learning via Maximum Acyclic Subgraph , Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:209-220, 2020.
• Yue Yu, Jie Chen, Tian Gao, Mo Yu. DAG-GNN: DAG Structure Learning with Graph Neural Networks, Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7154-7163, 2019.
• Jussi Viinikka. The Intersection-Validation Method for Evaluating Bayesian Network Structure Learning Without Ground Truth, Masters Thesis, University of Helsinki, 2019.
• Draaisma et al. Molecular Evolution of IDH Wild-Type Glioblastomas Treated With Standard of Care Affects Survival and Design of Precision Medicine Trials: A Report From the EORTC 1542 Study, Journal of Clinical Oncology, on-line: Nov 19, 2019.
• Topi Talvitie, Ralf Eggeling and Mikko Koivisto. Learning Bayesian networks with local structure, mixed variables, and exact algorithms, International Journal of Approximate Reasoning, 115:69-95, 2019.
• Francesco Maura et al. Genomic landscape and chronological reconstruction of driver events in multiple myeloma, Nature Communications 10, Article number 3835, 2019.
• Ralf Eggeling, Jussi Viinikka, Aleksis Vuoksenmaa and Mikko Koivisto. On Structure Priors for Learning Bayesian Networks, Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS) 2019, Naha, Okinawa, Japan. PMLR: Volume 89
• Zhenyu A. Liao, Charupriya Sharma, James Cussens and Peter van Beek. Finding All Bayesian Network Structures within a Factor of Optimal, Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), 2019.
• Jacob Grinfield et al. Classification and Personalized Prognosis in Myeloproliferative Neoplasms, New England Journal of Medicine 379:1416-1430, 2018.
• Adarsh Barik and Jean Honorio. Learning discrete Bayesian networks in polynomial time and sample complexity, arXiv:1803.04087, 2018
• Jan Švorc and and Jiří Vomlel. Employing Bayesian Networks for Subjective Well-being Prediction, Proc WUPES-18, 2018.
• Lilia Costa, James Q. Smith and Thomas Nichols. A group analysis using the Multiregression Dynamic Models for fMRI networked time series, Journal of Statistical Planning and Inference, 198:43-61, January 2019.
• Xun Zheng, Bryon Aragam, Pradeep Ravikumar and Eric P. Xing. DAGs with NO TEARS: Continuous Optimization for Structure Learning, Proc. NeurIPS 2018
• Antti Hyttinen, Johan Pensar, Juha Kontinen and Jukka Corander. Structure Learning for Bayesian Networks over Labeled DAGs, Proc. PGM'18, PMLR 72:133-144, 2018
• Andrew Li and Peter van Beek. Bayesian Network Structure Learning with Side Constraints, Proc. PGM'18, PMLR 72:225-236, 2018
• Shahab Behjati and Hamid Beigy. An Order-based Algorithm for Learning Structure of Bayesian Networks, Proc. PGM'18, PMLR 72:25-36, 2018
• Jussi Viinikka, Ralf Eggeling and Mikko Koivisto. Intersection-Validation: A Method for Evaluating Structure Learning without Ground Truth , Proc. AISTATS, 2018
• Brandon Malone, Kustaa Kangas, Matti Järvisalo, Mikko Koivisto and Petri Myllymäki. Empirical hardness of finding optimal Bayesian network structures: algorithm selection and runtime prediction , Machine Learning 107(1):247-283, 2018
• Samuel Vozza. Extending GOBNILP to learn Gaussian Bayesian Networks, MEng Project Dissertation, Dept of Computer Science, University of York, May 2017
• Scott McGregor. Exact Bayesian Network Learning in R, MSc Computing Project Dissertation, Dept of Computer Science, University of York, 2017
• John W. Cook, David Danks and Sergey M. Plis. Learning Dynamic Structure from Undersampled Data , Causality Workshop, UAI 2017
• Colin Lee and Peter van Beek. An Experimental Analysis of Anytime Algorithms for Bayesian Network Structure Learning, Proceedings of Machine Learning Research, Volume 73: Advanced Methodologies for Bayesian Networks (AMBN-2017), Kyoto, Japan, September, 2017.
• Colin Lee and Peter van Beek. Metaheuristics for Score-and-Search Bayesian Network Structure Learning, Proceedings of the 30th Canadian Conference on Artificial Intelligence, Edmonton, Alberta, May, 2017.
• Lilia Costa, Thomas Nichols and Jim Q. Smith. Studying the Effective Brain Connectivity using the Multiregression Dynamic Models , Brazilian Journal of Probability and Statistics, 2017 (to appear).
• Tian Gao, Kshitij Fadnis and Murray Campbell. Local-to-Global Bayesian Network Structure Learning, Proc. ICML 2017, 2017.
• Siqi Nie, Cassio P. de Campos and Qiang Ji. Efficient learning of Bayesian networks with bounded tree-width, International Journal of Approximate Reasoning Volume 80, Pages 412-427, January 2017.
• Eunice Yuh-Jie Chen, Yujia Shen, Arthur Choi and Adnan Darwiche Learning Bayesian networks with ancestral constraints, Proc. NIPS 2016, 2016.
• Tameem Adel and Cassio P. de Campos. Learning Bayesian Networks with Incomplete Data by Augmentation, arXiv 1608.07734, Oct 2016
• Milan Studený and James Cussens. The Chordal Graph Polytope for Learning Decomposable Models. Proc. PGM 2016 (JMLR: Workshop and Conference Proceedings vol 52), 499-510, 2016.
• Chris. J. Oates and Jim Q. Smith and Sach Mukherjee. Estimating Causal Structure Using Conditional DAG Models, Journal of Machine Learning Research 17:54 1-23, 2016.
• Mauro Scanagatta, Giorgio Corani, Cassio P. de Campos and Marco Zaffalon. Learning Bounded Treewidth Bayesian Networks with Thousands of Variables, Arxiv 1605.03392, 2016.
• Giorgio Corani and Mauro Scanagatta. Air pollution prediction via multi-label classification, Environmental Modelling and Software, 80:259-264, 2016.
• Azmi Hafizha Rahman Z.A., M. Syahrul Mubarok and Adiwijaya. Learning Struktur Bayesian Networks menggunakan Novel Modified Binary Differential Evolution pada Klasifikasi Data, Ind. Symposium on Computing pp. 265-278, Sept 2016.
• Siqi Nie, Cassio P. de Campos, and Qiang Ji. Learning Bayesian Networks with Bounded Tree-width via Guided Search, In AAAI Conference on Artificial Intelligence (AAAI), 2016.
• Yonatan Halpern. Semi-Supervised Learning for Electronic Phenotyping in Support of Precision Medicine, PhD Thesis, New York University, 2016.
• Yuh-Jie Chen. Learning Bayesian Network Structures with Non-Decomposable Scores, PhD Thesis, UCLA, 2016.
• Meng Sun. Estimating relationships and relatedness from dense genome-wide data PhD thesis, University of Leicester, 2015.
• Brandon Malone. Empirical Behavior of Bayesian Network Structure Learning Algorithms, Proc. Advanced Methodologies for Bayesian Networks: Second International Workshop, AMBN 2015, Yokohama, Japan, November 16-18, Springer, 2015.
• Yoni Halpern, Steven Horng and David Sontag. Anchored Discrete Factor Analysis . Arxiv 1511.03299, Nov 2015.
• Paul Saikko. Re-implementing and Extending a Hybrid SAT-IP Approach to Maximum Satisfiability . MSc thesis, University of Helsinki, Nov 2015.
• Janne H. Korhonen and Pekka Parviainen. Tractable Bayesian Network Structure Learning with Bounded Vertex Cover Number . NIPS 2015: Proceedings of the 29th Annual Conference on Neural Information Processing Systems, 2015
• Mauro Scanagatta, Cassio P. de Campos, Giorgio Corani and Marco Zaffalon. Learning Bayesian networks with thousands of variables . NIPS 2015: Proceedings of the 29th Annual Conference on Neural Information Processing Systems, 2015
• Pekka Parviainen and Samuel Kaski. Bayesian Networks for Variable Groups. Arxiv 1508.07753, August, 2015
• Arnoud Pastink and Linda van der Gaag. Multi-classifiers of Small Treewidth. Proc. ECSQARU 2015, LNAI 9161, pp. 199-209, 2015.
• Peter van Beek and Hella-Franziska Hoffmann. Machine learning of Bayesian networks using constraint programming. Proceedings of CP 2015, Cork, Ireland, August, 2015.
• Vera Djordjilović, Monica Chiogna and Jirka Vomlel. An empirical comparison of popular algorithms for learning gene networks. Proc. CEB-EIB 2015, 2015
• Chris J. Oates, Lilia Carneiro da Costa and Tom Nichols. Towards a Multi-Subject Analysis of Neural Connectivity. Neural Computation, 27:151-170, 2015. (Also Arkiv 1404.1239, April 2014.)
• Waleed Alsanie and James Cussens. Learning Failure-free PRISM Programs. International Journal of Approximate Reasoning, 67:73-110, 2015.
• Brandon Malone, Matti Järvisalo and Petri Myllymäki. Impact of Learning Strategies on the Quality of Bayesian Networks: An Empirical Evaluation. Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence (UAI-15) July 2015.
• Mark Bartlett and James Cussens. Integer Linear Programming for the Bayesian Network Structure Learning Problem. Artificial Intelligence. Forthcoming. (Preprint)
• Chris J. Oates, Jim Q. Smith, Sach Mukherjee and James Cussens. Exact Estimation of Multiple Directed Acyclic Graphs. Statistics and Computing, Forthcoming. (Arkiv 1404.1238, April 2014.)
• Paul Saikko, Brandon Malone and Matti Järvisalo. MaxSAT-based Cutting Planes for Learning Graphical Models. Proc. CPAIOR 2015
• Kustaa Kangas, Teppo Niinimäki and Mikko Koivisto. Learning Chordal Markov Networks by Dynamic Programming. NIPS 2014.
• Chris J. Oates, Jim Q. Smith, Sach Mukherjee. Estimating causal structure using conditional DAG models. Arkiv 1411.2755, November 2014.
• Giorgio Corani, Alessandro Antonucci, Denis D. Maua and Sandra Gabaglio. Trading off Speed and Accuracy in Multilabel Classification Proc. PGM 2014, 145-159, LNAI 8754, Springer, 2014. (Proceedings).
• Mauro Scanagatta, Cassio P. de Campos and Marco Zaffalon. Min-BDeu and Max-BDeu Scores for Learning Bayesian Networks. Proc. PGM 2014, 426-441, LNAI 8754, Springer, 2014. (Preprint). (Proceedings).
• Ilya Korsunsky, Daniele Ramazzotti, Giulio Caravagna and Bud Mishra. Inference of Cancer Progression Models with Biological Noise. Arkiv 1408.6032, August 2014
• Lilia Costa, Jim Smith, Thomas Nichols and James Cussens. Searching Multiregression Dynamic Models of Resting-State fMRI Networks using Integer Programming. Bayesian Analysis. To appear.
• Nuala Sheehan, Mark Bartlett and James Cussens. Improved Maximum Likelihood Reconstruction of Complex Multi-generational Pedigrees. Theoretical Population Biology. 97:11-19, November 2014.
• Brandon Malone, Kustaa Kangas, Matti Järvisalo, Mikko Koivisto, and Petri Myllymäki. Predicting the Hardness of Learning Bayesian Networks. In Carla E. Brodley and Peter Stone, editors, Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI 2014) AAAI Press, 2014.
• Alberto Franzin. An Integer Programming approach to Bayesian Network Structure Learning. Masters dissertation, University of Padua, April 2014.
• Milan Studený, David Haws Learning Bayesian network structure: Towards the essential graph by integer linear programming tools International Journal of Approximate Reasoning, 2013
• Changhe Yuan and Brandon Malone Learning Optimal Bayesian Networks: A Shortest Path Perspective, Journal of Artificial Intelligence Research, Volume 48, pages 23-65, 2013
• Pekka Parviainen and Mikko Koivisto. Finding Optimal Bayesian Networks Using Precedence Constraints. Journal of Machine Learning Resarch. vol 14. pp1387-1415, 2013.
• Eman Aljohani and James Cussens. Informative Priors For Learning Graphical Models. Proc. AIGM-13, Paris, 2013.
• Antonucci A., Corani G., Maua D., Gabaglio S., An Ensemble of Bayesian Networks for Multilabel Classification. Proc. 23rd Int. Joint Conference on Artificial Intelligence (IJCAI-13)
• Brandon Malone and Changhe Yuan. Evaluating Anytime Algorithms for Learning Optimal Bayesian Networks . Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI-13) July 2013.
• Eliot Brenner and David Sontag. SparsityBoost: A New Scoring Function for Learning Bayesian Network Structure. Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI-13) July 2013.
• Mark Bartlett and James Cussens. Advances in Bayesian Network Learning using Integer Programming. Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI-13) July 2013.
• Waleed Alsanie. Learning Failure-free PRISM Programs. PhD Thesis, University of York, September 2012.
• Zhenxing Wang and Laiwan Chan. Learning Bayesian Networks from Markov Random Fields: An Efficient Algorithm for Linear Models. ACM Transactions on Knowledge Discovery from Data (TKDD). 6(3) 2012 10:1-10:31.
• Robert G. Cowell. A simple greedy algorithm for reconstructing pedigrees. Theoretical Population Biology. 83 (2013) 55-63.
• James Cussens. Bayesian network learning with cutting planes. In Fabio G. Cozman and Avi Pfeffer, editors, Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), pages 153-160, Barcelona, 2011. AUAI Press. This paper introduces the basic idea of the GOBNILP approach and uses a preliminary version of the software.