Ricardo@CMU

Ricardo@CMU

Ricardo Silva
CALD - Center for Automated Learning and Discovery
Wean Hall 3717
Carnegie Mellon University
(412) 268-5521
rbas@cs.cmu.edu

Fast facts:
I am finishing the Computational and Statistical Learning PhD program at Carnegie Mellon University. My primary work is in causality discovery from observational data with Richard Scheines, Clark Glymour and Peter Spirtes. Currently, I am moving to London for a couple of years as a post-doc. THIS HOMEPAGE IS OUT-OF-DATE. The current one is at Gatsby.

Under review

• Learning the structure of linear latent variable models . With Richard Scheines, Clark Glymour and Peter Spirtes. A formal approach for identifying structural features in linear graphs where no conditional independencies are observable. To the best of our knowledge, this the first work to state theoretical guarantees of identifiability;

Selected publications

• 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.; 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. 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