Ricardo Silva Statslab, Centre for Mathematical Sciences Wilberforce Road, Cambridge CB3 0WB University of Cambridge silva [at] statslab [dot] cam [dot] ac [dot] uk Fast facts: I am a postdoctoral research fellow at Cambridge working on Bayesian approaches for graphical latent variable models and causal inference.

Software
 

XGP: a new Gaussian process relational classifier. MATLAB implementation for a new family of transductive classifiers using relational information.

dmgBayes: software for Bayesian inference in mixed graph models. Basic functionality for Gaussian modeling with mixed graph models. Other probabilistic models are planned.

RankSearch: Bayesian structure learning for latent variable models. Learn latent variable models without pointless independence/tree-structure assumptions on the latent structure.

BuildPureClusters: learning to measure hidden common causes. An approach for building measurement models of hidden common causes from data without specifying the hidden common causes a priori. Part of the Tetrad project.

New manuscripts  

Silva, R. and Ghahramani, Z. (2008). The hidden life of latent variables: Bayesian learning with mixed graph models (14/12/07 draft)

Silva, R.; Airoldi, A. and Heller, K. (2007). Small sets of interacting proteins suggest latent linkage mechanisms through analogical reasoning. Gatsby Computational Neuroscience Unit, Technical Report GCNU TR 2007-001. Software

Publications
 

Silva, R.; Chu, W. and Ghahramani, Z. (2007). Hidden common cause relations in relational learning. Neural Information Processing Systems, NIPS 2007. [Code] [Data] [Poster]

Silva, R. (2007). Causality (available soon). In Encyclopedia of Machine Learning. Claude Sammut, ed. Springer-Verlag. ISBN: 978-0387307688

Silva, R.; Heller, K. and Ghahramani, Z. (2007). Analogical reasoning with relational Bayesian sets. 11th International Conference on Artificial Intelligence and Statistics, AISTATS 2007.

Silva, R. and Scheines, R. (2006). Towards association rules with hidden variables. Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2006.

Silva, R. and Ghahramani, Z. (2006). Bayesian inference for Gaussian mixed graph models. Proceedings of the 22nd Conference on Uncertainty on Artificial Intelligence, UAI 2006. [Code]

Silva, R. and Scheines, R. (2006). Bayesian learning of measurement and structural models. Proceedings of the 23rd International Conference on Machine Learning, ICML 2006. [Code]

Silva, R.; Scheines, R.; Glymour, C. and Spirtes, P. (2006). Learning the structure of linear latent variable models. Journal of Machine Learning Research 7(Feb):191--246, 2006. [Code]

Silva, R. and Scheines, R. (2005). New d-separation identification results for learning continuous latent variable models. Proceedings of the International Conference in Machine Learning, ICML 05. Tech report version.

Silva, R.; Zhang, J. and Shanahan, J. G. (2005). Probabilistic workflow mining. Proceedings of Knowledge Discovery and Data Mining, KDD 05. Tech report version.

Silva, R. (2005). Automatic Discovery of Latent Variable Models . PhD Thesis. Machine Learning Department, Carnegie Mellon University.

Silva, R.; Scheines, R.; Glymour, C. and Spirtes P. (2003) "Learning measurement models for unobserved variables". Proceedings of the 19th Conference on Uncertainty on Artificial Intelligence.

Moody, J.; Silva, R.; Vanderwaart, J.; Ramsey, J. and Glymour, C. (2002). Classification and filtering of spectra: A case study in mineralogy. Intelligent Data Analysis 6 (6), 517-530.

Moody, J.; Silva, R.; Vanderwaart, J. and Glymour, C. (2001). "Data filtering for automatic classification of rocks from reflectance spectra". Proceedings of the 7th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, p. 347-352. ACM Press, San Francisco, CA. 

Silva, R. B. A. and Ludermir, T. B. (2001). “Hybrid systems of local basis functions”. Intelligent Data Analysis 5 (3), 227-244

Silva, R. B. A. and Ludermir, T. B. (2000). “Obtaining simplified rules by hybrid learning”. Proceedings of the 17th International Conference on Machine Learning, 879-886. Morgan Kaufmann, San Francisco, CA

Other publications/reports