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Kinsler, P., Holman, S., Elliott, A. , Mitchell, C. N. and Wilson, R. E. (2024) Agent swarms: cooperation and coordination under stringent communications constraint. PLoS ONE, 19(12), e0311513. (doi: 10.1371/journal.pone.0311513) (PMID:39661582) (PMCID:PMC11633978)
Abroshan, M., Elliott, A. and Khalili, M. M. (2024) Imposing Fairness Constraints in Synthetic Data Generation. In: 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024), Valencia, Spain, 2-4 May 2024, pp. 2269-2277.
Ouyang, R., Elliott, A. , Limnios, S., Cucuringu, M. and Reinert, G. (2024) L2G2G: A scalable local-to-global network embedding with graph autoencoders. In: Complex Networks 2023. Series: Studies in computational intelligence (1141). Springer. (Accepted for Publication)
Law, S., Hasegawa, R., Paige, B., Russell, C. and Elliott, A. (2023) Explaining holistic image regressors and classifiers in urban analytics with plausible counterfactuals. International Journal of Geographical Information Science, 37, pp. 2575-2596. (doi: 10.1080/13658816.2023.2214592)
Houssiau, F., Jordon, J., Cohen, S., Elliott, A. , Geddes, J., Mole, C., Rangel-Smith, C. and Szpruch, L. (2022) TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Data. NeurIPS 2022 Workshop on Synthetic Data for Empowering ML Research, New Orleans, Louisiana, United States, 2 December 2022.
Limnios, S., Elliott, A. , Cucuringu, M. and Reinert, G. D. (2022) Random Walk based Conditional Generative Model for Temporal Networks with Attributes. NeurIPS 2022 Workshop on Synthetic Data for Empowering ML Research, New Orleans, Louisiana, United States, 2 December 2022.
Clarkson, J., Cucuringu, M., Elliott, A. and Reinert, G. (2022) DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series. In: LOG 2022 Learning on Graphs Conference, 9-12 December 2022,
Elliott, A. , Law, S. and Russell, C. (2021) Explaining Classifiers Using Adversarial Perturbations on the Perceptual Ball. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 11-17 Oct 2021, pp. 10688-10697. (doi: 10.1109/CVPR46437.2021.01055)
Andersson, T. R. et al. (2021) Seasonal Arctic sea ice forecasting with probabilistic deep learning. Nature Communications, 12, 5124. (doi: 10.1038/s41467-021-25257-4) (PMID:34446701) (PMCID:PMC8390499)
Elliott, A. , Chiu, A., Bazzi, M., Reinert, G. and Cucuringu, M. (2020) Core–periphery structure in directed networks. Proceedings of the Royal Society of London Series A: Mathematical, Physical and Engineering Sciences, 476(2241), 20190783. (doi: 10.1098/rspa.2019.0783) (PMID:33061788) (PMCID:PMC7544362)
Underwood, W. G., Elliott, A. and Cucuringu, M. (2020) Motif-based spectral clustering of weighted directed networks. Applied Network Science, 5, 62. (doi: 10.1007/s41109-020-00293-z)
Elliott, A. , Leicht, E., Whitmore, A., Reinert, G. and Reed-Tsochas, F. (2018) A nonparametric significance test for sampled networks. Bioinformatics, 34(1), pp. 64-71. (doi: 10.1093/bioinformatics/btx419) (PMID:29036452) (PMCID:PMC5870844)
Mas, I. and Elliott, A. (2014) Where's the Cash? The Geography of Cash Points in Tanzania. [Research Reports or Papers]
Kinsler, P., Holman, S., Elliott, A. , Mitchell, C. N. and Wilson, R. E. (2024) Agent swarms: cooperation and coordination under stringent communications constraint. PLoS ONE, 19(12), e0311513. (doi: 10.1371/journal.pone.0311513) (PMID:39661582) (PMCID:PMC11633978)
Law, S., Hasegawa, R., Paige, B., Russell, C. and Elliott, A. (2023) Explaining holistic image regressors and classifiers in urban analytics with plausible counterfactuals. International Journal of Geographical Information Science, 37, pp. 2575-2596. (doi: 10.1080/13658816.2023.2214592)
Andersson, T. R. et al. (2021) Seasonal Arctic sea ice forecasting with probabilistic deep learning. Nature Communications, 12, 5124. (doi: 10.1038/s41467-021-25257-4) (PMID:34446701) (PMCID:PMC8390499)
Elliott, A. , Chiu, A., Bazzi, M., Reinert, G. and Cucuringu, M. (2020) Core–periphery structure in directed networks. Proceedings of the Royal Society of London Series A: Mathematical, Physical and Engineering Sciences, 476(2241), 20190783. (doi: 10.1098/rspa.2019.0783) (PMID:33061788) (PMCID:PMC7544362)
Underwood, W. G., Elliott, A. and Cucuringu, M. (2020) Motif-based spectral clustering of weighted directed networks. Applied Network Science, 5, 62. (doi: 10.1007/s41109-020-00293-z)
Elliott, A. , Leicht, E., Whitmore, A., Reinert, G. and Reed-Tsochas, F. (2018) A nonparametric significance test for sampled networks. Bioinformatics, 34(1), pp. 64-71. (doi: 10.1093/bioinformatics/btx419) (PMID:29036452) (PMCID:PMC5870844)
Ouyang, R., Elliott, A. , Limnios, S., Cucuringu, M. and Reinert, G. (2024) L2G2G: A scalable local-to-global network embedding with graph autoencoders. In: Complex Networks 2023. Series: Studies in computational intelligence (1141). Springer. (Accepted for Publication)
Mas, I. and Elliott, A. (2014) Where's the Cash? The Geography of Cash Points in Tanzania. [Research Reports or Papers]
Houssiau, F., Jordon, J., Cohen, S., Elliott, A. , Geddes, J., Mole, C., Rangel-Smith, C. and Szpruch, L. (2022) TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Data. NeurIPS 2022 Workshop on Synthetic Data for Empowering ML Research, New Orleans, Louisiana, United States, 2 December 2022.
Limnios, S., Elliott, A. , Cucuringu, M. and Reinert, G. D. (2022) Random Walk based Conditional Generative Model for Temporal Networks with Attributes. NeurIPS 2022 Workshop on Synthetic Data for Empowering ML Research, New Orleans, Louisiana, United States, 2 December 2022.
Abroshan, M., Elliott, A. and Khalili, M. M. (2024) Imposing Fairness Constraints in Synthetic Data Generation. In: 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024), Valencia, Spain, 2-4 May 2024, pp. 2269-2277.
Clarkson, J., Cucuringu, M., Elliott, A. and Reinert, G. (2022) DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series. In: LOG 2022 Learning on Graphs Conference, 9-12 December 2022,
Elliott, A. , Law, S. and Russell, C. (2021) Explaining Classifiers Using Adversarial Perturbations on the Perceptual Ball. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 11-17 Oct 2021, pp. 10688-10697. (doi: 10.1109/CVPR46437.2021.01055)
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