| Valuing the manual: The demarcation of embodied practices within algorithmic decision-making processes DG Beer, A Wallace, R Burrows, A Ciocanel, J Cussens Social and Cultural Geography, 2024 | | 2024 |
| Open Banking and data reassurance: the case of tenant referencing in the UK A Ciocănel, A Wallace, D Beer, J Cussens, R Burrows Information, Communication & Society, 1-16, 2024 | 1 | 2024 |
| Automation hesitancy: confidence deficits, established limits and notional horizons in the application of algorithms within the private rental sector in the UK D Beer, A Wallace, A Ciocanel, R Burrows, J Cussens Information, Communication & Society, 1-16, 2023 | 1 | 2023 |
| Branch-Price-and-Cut for Causal Discovery J Cussens Conference on Causal Learning and Reasoning, 642-661, 2023 | | 2023 |
| Algorithmic risk profiling in housing: a literature review A Ciocanel, DG Beer, R Burrows, J Cussens, A Wallace | | 2022 |
| Socio-technical imaginaries of data shaping access to housing A Ciocanel, A Wallace, DG Beer, B Roger, J Cussens ENHR Workshop on ‘Late Homeownership’, 2022 | | 2022 |
| The dual polyhedron to the chordal graph polytope and the rebuttal of the chordal graph conjecture M Studený, J Cussens, V Kratochvíl International Journal of Approximate Reasoning 138, 188-203, 2021 | 4 | 2021 |
| Kernel-based Approach for Learning Causal Graphs from Mixed Data J Cussens Proceedings of the 10th International Conference on Probabilistic Graphical …, 2020 | | 2020 |
| Dual Formulation of the Chordal Graph Conjecture J Cussens Proceedings of the 10th International Conference on Probabilistic Graphical …, 2020 | | 2020 |
| A Score-and-Search Approach to Learning Bayesian Networks with Noisy-OR Relations J Cussens Proceedings of the 10th International Conference on Probabilistic Graphical …, 2020 | | 2020 |
| Learning All Credible Bayesian Network Structures for Model Averaging ZA Liao, C Sharma, J Cussens, P van Beek arXiv preprint arXiv:2008.13618, 2020 | | 2020 |
| On pruning for score-based Bayesian network structure learning AHC Correia, J Cussens, C de Campos International conference on artificial intelligence and statistics, 2709-2718, 2020 | 14 | 2020 |
| GOBNILP: Learning Bayesian network structure with integer programming J Cussens International Conference on Probabilistic Graphical Models, 605-608, 2020 | 13 | 2020 |
| Dual formulation of the chordal graph conjecture M Studeny, J Cussens, V Kratochvil International Conference on Probabilistic Graphical Models, 449-460, 2020 | 1 | 2020 |
| Kernel-based approach for learning causal graphs from mixed data T Handhayani, J Cussens International Conference on Probabilistic Graphical Models, 221-232, 2020 | 7 | 2020 |
| A score-and-search approach to learning Bayesian networks with noisy-or relations C Sharma, ZA Liao, J Cussens, P Beek International Conference on Probabilistic Graphical Models, 413-424, 2020 | 3 | 2020 |
| Fast online learning in the presence of latent variables D Kocacoban, J Cussens Digitale Welt 4, 37-42, 2020 | 6 | 2020 |
| Online causal structure learning in the presence of latent variables D Kocacoban, J Cussens 2019 18th IEEE International Conference On Machine Learning And Applications …, 2019 | 4 | 2019 |
| Kernel-based approach to handle mixed data for inferring causal graphs T Handhayani, J Cussens arXiv preprint arXiv:1910.03055, 2019 | 4 | 2019 |
| Finding all Bayesian network structures within a factor of optimal ZA Liao, C Sharma, J Cussens, P van Beek Proceedings of the AAAI conference on artificial intelligence 33 (01), 7892-7899, 2019 | 25 | 2019 |
Mark BartlettSchool of Computing, Robert Gordon UniversityVerified email at rgu.ac.uk
