Ricardo@UCL
Ricardo Silva
Department of Statistical Science, UCL
Gower Street - London - WC1E 6BT
University College London
ricardo [-at-] stats [dot] ucl [dot] ac [dot] uk

Fast facts:
I am a Lecturer in Statistics in UCL working on computational approaches for graphical latent variable models, relational models and causal inference.
 

Departmental Tutor office hours:
Usually Mondays 11am-noon, Wednesdays noon-1pm, Fridays 2pm-3pm. In general I will not be available at other times without an appointment. My office is number 139 at the 1-19 Torrington Place building (corner of Torrington Place with Tottenham Court Road).
 

Submit to the 2015 UAI Workshop, "Advances in Causal Inference"

CV

Software
 
  • WPP: Causal inference through a Witness Protection Program. A method for inferring bounds on average causal effects based on a combination of linear programming and Bayesian inference. R code.

  • XCOP: Dirichlet process mixture of tree-structured copulas. MCMC method for Bayesian inference with multivariate copulas and missing data. R/C++ code.

  • GPSEM: Gaussian process structural equation models with latent variables. Samples latent variables and parameters from the posterior distribution. MATLAB/C++ code.

  • XGP: a new Gaussian process relational classifier. A new family of transductive classifiers using relational information. MATLAB code.

  • dmgBayes: software for Bayesian inference in mixed graph models. Basic functionality for Gaussian modeling with mixed graph models. Java code.

  • RankSearch: Bayesian structure learning for latent variable models. Sparse factor model learning with dependent latent variables. Java code.

  • 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.

  • Publications
     
  • Silva, R.; Kang, S. M. and Airoldi, E. M. (2015). Predicting traffic volumes and estimating the effects of shocks in massive transportation systems Proceedings of the National Academy of Sciences of the United States of America, in press.

  • Silva, R. (2015). Bayesian inference in cumulative distribution fields In Polpo, Louzada, Rifo, Stern and Lauretto (eds.), Interdisciplinary Bayesian Statistics, pp.83-95, EBEB 2014, Springer.

  • Silva, R. and Evans, R. (2014). Causal inference through a witness protection program Advances in Neural Information Processing Systems, NIPS. [Code] [arXiv]

  • Silva, R. (2014). Causality In G. Webb and C. Sammut, eds., Springer Encyclopedia of Machine Learning and Data Mining, to appear (this is an update of the 2010 article below). [Tutorial@Imperial College]

  • Kalaitzis, A. and Silva, R. (2013). Flexible sampling of discrete data correlations without the marginal distributions Advances in Neural Information Processing Systems, NIPS. [Supplementary Material]

  • Silva, R. (2013). A MCMC approach for learning the structure of Gaussian acyclic directed mixed graphs In Giudici, Ingrassia and Vichi, eds., Statistical Models for Data Analysis. Springer. [Draft] [Code]

  • Sanborn, A. and Silva, R. (2013). Constraining bridges between levels of analysis: A computational justification for Locally Bayesian Learning. Journal of Mathematical Psychology, 57, 94-106. [Manuscript]

  • Silva, R. (2012). Latent composite likelihood learning for the structured canonical correlation model . Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, UAI 2012. [Supplementary Material] [Code] [Poster]

  • Silva, R. (2011). Thinning measurement models and questionnaire design . Advances in Neural Information Processing Systems 24, NIPS 2011. [Supplementary Material] [Code]

  • Zhang, J. and Silva, R. (2011). Discussion of ``Learning Equivalence Classes of Acyclic Models with Latent and Selection Variables from Multiple Datasets with Overlapping Variables.''. Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, AISTATS 2011. [Slides]

  • Silva, R; Blundell, C. and Teh, Y.W. (2011). Mixed cumulative distribution networks . Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, AISTATS 2011. [Supplementary Material] [Code (Coming Soon)] [Slides]

  • Silva, R. (2010). Causality. In Encyclopedia of Machine Learning. Claude Sammut, ed. Springer-Verlag. ISBN: 978-0387-30768-8

  • Silva, R. and Gramacy, R. (2010). Gaussian process structural equation models with latent variables . Proceedings of the 26th Conference on Uncertainty on Artificial Intelligence, UAI 2010. [Code] [Slides]

  • Silva, R. (2010). Measuring latent causal structure. P. McKay Illari, F. Russo and J. Williamson (eds.), Causality in the Sciences, to appear. [Draft]

  • Silva, R.; Heller, K.; Ghahramani, Z. and Airoldi, E. (2010). Ranking relations using analogies in biological and information networks. Annals of Applied Statistics, to appear. [arXiv]

  • Silva, R. and Ghahramani, Z. (2009). The hidden life of latent variables: Bayesian learning with mixed graph models. Journal of Machine Learning Research 10, 1187--1238. [Code]

  • Sanborn, A. N. and Silva, R. (2009). Belief propagation and locally Bayesian learning. 31st Annual Conference of the Cognitive Science Society.

  • Silva, R. and Gramacy, R. (2009). MCMC methods for Bayesian mixtures of copulas. Artificial Intelligence and Statistics, AISTATS 2009. [Code]

  • Silva, R. and Ghahramani, Z. (2009). Factorial mixture of Gaussians and the marginal independence model. Artificial Intelligence and Statistics, AISTATS 2009. [Code]

  • 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.; 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: June 07th, 2011