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 Professor of Statistical Machine Learning and Data Science, working on computational approaches for causal inference, graphical latent variable models and relational models.
 


CV

Software
 
  • ObsInt. A simple approach for learning from observational and experimental data using Gaussian processes. MATLAB code.

  • CausalFX: Machine learning for causal inference with observational data. Methods for inferring causal effects based on a combination of linear programming and Bayesian inference. Also available from CRAN. Old code to reproduce our NIPS paper on the Witness Protection Program here.

  • 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.

  • Manuscripts (more on Google Scholar)
     
  • Colombo, N.; Silva, R.; Kang, S. M. and Gretton, A. (2019) Counterfactual Distribution Regression for Structured Inference . August, 2019.

  • Loftus, J.; Russell,C.; Kusner, M. and Silva, R. (2018) Causal reasoning for algorithmic fairness . May, 2018.

  • Regli, J.-B. and Silva, R. (2018) Alpha-Beta Divergence For Variational Inference . May, 2018.

  • Ng, Y. C. and Silva, R.(2017) A dynamic edge exchangeable model for sparse temporal networks . October, 2017.

  • Selected Talks
     
  • Silva, R. (2015). Bayesian Networks and the Search for Causality. October, 2015. Talk given at the London Bayesian Networks Meetup [Event]

  • Co-organized Events
     
  • Uncertainty in Artificial Intelligence 2019, Tel Aviv, Israel.

  • Uncertainty in Artificial Intelligence 2018, Monterey, California, USA.

  • From 'What If?' To 'What Next?': Causal Inference and Machine Learning for Intelligent Decision Making, part of NIPS 2017, Long Beach, USA.

  • What If? Inference and Learning of Hypothetical and Counterfactual Interventions in Complex Systems, part of NIPS 2016, Barcelona, Spain.

  • 2016 Network on Computational Statistics and Machine Learning Workshop, "Data Science: pulling together computational statistics and machine learning?", University of Edinburgh.

  • 2016 UAI Workshop, "Causality: Foundation to Application", Jersey City, New Jersey USA.

  • 2015 Network on Computational Statistics and Machine Learning Workshop, "Autonomous Citizens: Algorithms for Tomorrow's Society", University of Warwick.

  • 2015 UAI Workshop, "Advances in Causal Inference", Amsterdam, Netherlands.

  • 2014 Network on Computational Statistics and Machine Learning Workshop, "Big Data, Big Models, it is a Big Deal", University of Warwick.

  • Centre for Computational Statistics and Machine Learning Masterclasses (on-going series), University College London.

  • Publications
     
  • Whitaker, G. A.; Silva, R; Edwards, D. and Kosmidis, I. (2021) A Bayesian inference approach for determining player abilities in soccer . Journal of the Royal Statistical Society Series C, to appear.

  • Bartlett, T.; Kosmidis, I. and Silva, R. (2021) Two-way sparsity for time-varying networks, with applications in genomics . Annals of Applied Statistics, to appear.

  • Kilbertus, N.; Kusner, M. and Silva, R. (2020) A class of algorithms for general instrumental variable models. Advances in Neural Information Processing Systems 33 (NeurIPS2020).

  • Chilinski, P. and Silva, R. (2020) Neural likelihoods via cumulative distribution functions . Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI2020).

  • Saengkyongam, S. and Silva, R. (2020) Learning joint nonlinear effects from single-variable interventions in the presence of hidden confounders. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI2020).

  • Gultchin, L.; Kusner, M.; Kanade, V. and Silva, R. (2020) Differentiable causal backdoor discovery. Proceedings of the 23rd International Conference on Artificial Intelligence and Statisgics (AISTATS2020).

  • Kilbertus, N.; Ball, P.; Kusner, M.; Weller, A., and Silva, R. (2019). The Sensitivity of Counterfactual Fairness to Unmeasured Confounding. Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI2019). [Supplement] [Code]

  • Kusner, M.; Russell, C.; Loftus, J.; and Silva, R. (2019). Making Decisions that Reduce Discriminatory Impacts. Proceedings of the 36th International Conference on Machine Learning, 3591-3600 (ICML2019).

  • Roa-Vicens, J.; Chtourou, C.; Filos, A.; Rullan, F.; Gal, Y. and Silva, R. (2019). Towards Inverse Reinforcement Learning for Limit Order Book Dynamics. Multi-Agent Learning Workshop at the 36th International Conference on Machine Learning.

  • Globerson, A. and Silva, R. (2018). Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence (UAI2018). AUAI Press, ISBN 978-0-9966431-3-9.

  • Whitaker, G; Silva, R. and Edwards, D. (2018). Visualizing a Team's Goal Chances in Soccer from Attacking Events: A Bayesian Inference Approach. Big Data 6(4), 271-290.

  • Ng, Y. C.; Colombo, N. and Silva, R. (2018). Bayesian semi-supervised learning with graph Gaussian processes. Advances in Neural Information Processing Systems (NeurIPS 2018).

  • Coutrot, A.; Silva, R.; Manley, E.; de Cothi, W.; Sami, S.; Bohbot, V.; Wiener, J.; Holscher, C.; Dalton, R. C.; Hornberger, M. and Spiers, H. (2018). Global determinants of navigation ability. Current Biology 28(17), 2861-2866.

  • Silva, R. and Shimizu, S. (2017). Learning instrumental variables with structural and non-Gaussianity assumptions. Journal of Machine Learning Research 18(120):1.49, 2017. [Code]

  • Russell, C.; Silva, R.; Kusner, M. and Loftus C. (2017) When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness.. Advances in Neural Information Processing Systems (NIPS) 30.

  • Kusner, M.; Loftus, C.; Russell, C. and Silva, R. (2017) Counterfactual Fairness. Advances in Neural Information Processing Systems (NIPS) 30. [Talk (not the NIPS one)] [New Scientist article 1] [New Scientist article 2] [Alan Turing Institute article]

  • Colombo, N.; Silva, R. and Kang, S. M. (2017). Tomography of the London Underground: a Scalable Model for Origin-Destination Data. Advances in Neural Information Processing Systems (NIPS) 30.

  • Carmo, R.; Kang, S. M. and Silva, R. (2017). Visualization of Topic-Sentiment Dynamics in Crowdfunding Projects. Sixteenth International Symposium on Intelligent Data Analysis (IDA 2017).

  • Eberhardt, F.; Bareinboim, E.; Maathuis, M.; Mooij, J. and Silva, R. eds. (2017). Proceedings of the UAI 2016 Workshop on Causation: Foundation to Application. Co-located with the 32st Conference on Uncertainty in Artificial Intelligence (UAI 2016). Jersey City, USA, June 29, 2016.

  • Silva, R. (2016). Observational-interventional priors for dose-response learning Advances in Neural Information Processing Systems, NIPS. [Supplement] [Code]

  • Ng, Y-C., Chilinsksi, P. and Silva, R. (2016). Scaling factorial hidden Markov models: stochastic variational inference without messages Advances in Neural Information Processing Systems, NIPS. [Supplement]

  • Silva, R. (2016). Comments on "Causal inference using invariant prediction: identification and confidence intervals" by Peters, Buhlmann and Meinshausen, JRSS B, 78, 991-992.

  • Silva, R. and Evans, R. (2016). Causal inference through a witness protection program Journal of Machine Learning Research 17(56):1-53, expansion of the 2014 NIPS paper. [Code]

  • Silva, R.; Shpitser, I.; Evans, R.; Peters, J. and Claassen, T., eds. (2015). Proceedings of the UAI 2015 Workshop on Advances in Causal Inference. Co-located with the 31st Conference on Uncertainty in Artificial Intelligence (UAI 2015). Amsterdam, The Netherlands, July 16, 2015.

  • Silva, R. and Kalaitzis, A. (2015). Bayesian inference via projections Statistics and Computing 25, 739-753. [Code coming soon] [Draft]

  • 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 112, 5643-5648.

  • 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: December 9th, 2020