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Ricardo@Cambridge
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.
 


Resume
Research Statement

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



    Last modification: April 14 2006