Publications¶

• Masegosa, A., Nielsen, T. D., Langseth, H., Ramos-López, D., Salmerón, A., & Madsen, A. L. (2017, July). Bayesian Models of Data Streams with Hierarchical Power Priors. In International Conference on Machine Learning (pp. 2334-2343) [Link].
• Dogadov, S., Masegosa, A., & Nakajima, S. (2017). Variational Robust Subspace Clustering With Mean Update Algorithm. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1792-1799) [Link].
• Ramos-López D. Masegosa, A., Nielsen, T. D., Langseth, H., Salmerón, A., & Madsen, A. L. Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks. International Journal of Approximate Reasoning. 100, 115-134, 2018 [Link].
• Masegosa, A. R., Martinez, A. M., Langseth, H., Nielsen, T. D., Salmerón, A., Ramos-López, D., & Madsen, A. L. (2017). Scaling up Bayesian variational inference using distributed computing clusters. International Journal of Approximate Reasoning, 88, 435-451 [Link].
• Cabañas, R., Cano, A., Gómez-Olmedo, M., Masegosa, A. R., & Moral, S. (2018, June). Virtual Subconcept Drift Detection in Discrete Data Using Probabilistic Graphical Models. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 616-628). Springer, Cham [Link].
• Cabanas, R., Cano, A., Gómez-Olmedo, M., & Antonucci, A. A Linear Programming Based Approach for Evaluating Interval-valued Influence Diagrams. XVIII Conferencia de la Asociacion Española para la Inteligencia Artificial. Granada 2018. [Link].