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Number of items: 40.

Article

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

Sanborn, AN; Silva, R; (2013) Constraining bridges between levels of analysis: A Computational justification for locally Bayesian learning. JOURNAL OF MATHEMATICAL PSYCHOLOGY , 57 (3-4) pp. 94-106. 10.1016/j.jmp.2013.05.002.

Silva, R; Evans, R; (2016) Causal Inference through a Witness Protection Program. Journal of Machine Learning Research , 17 , Article 56. Green open access
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Silva, R; Ghahramani, Z; (2009) The Hidden Life of Latent Variables: Bayesian Learning with Mixed Graph Models. JOURNAL OF MACHINE LEARNING RESEARCH , 10 pp. 1187-1238. Gold open access

Silva, R; Heller, K; Ghahramani, Z; Airoldi, EM; (2010) RANKING RELATIONS USING ANALOGIES IN BIOLOGICAL AND INFORMATION NETWORKS. ANNALS OF APPLIED STATISTICS , 4 (2) pp. 615-644. 10.1214/09-AOAS321.

Silva, R; Kalaitzis, A; (2015) Bayesian inference via projections. Statistics and Computing , 25 (4) pp. 739-753. 10.1007/s11222-015-9557-6. Green open access
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Silva, R; Kang, SM; Airoldi, EM; (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 (18) pp. 5643-5648. 10.1073/pnas.1412908112. Green open access
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Silva, R; Ludermir, T; (2001) Hybrid systems of local basis functions. Intelligent Data Analysis , 5 (3) pp. 227-244.

Silva, R; Scheines, R; Glymour, C; Spirtes, P; (2006) Learning the structure of linear latent variable models. JOURNAL OF MACHINE LEARNING RESEARCH , 7 pp. 191-246. Gold open access

Book chapter

Silva, R; (2011) Measuring latent causal structure. In: Illari, PM and Russo, F and Williamson, J, (eds.) Causality in the Sciences. (pp. 673-696). Oxford Univ Press: New York, US.

Silva, R; (2010) Causality. In: Sammut, C and Webb, G, (eds.) Encyclopedia of Machine Learning. Springer-Verlag

Proceedings paper

Carmo, R; Kang, S; Silva, R; (2017) Visualization of Topic-Sentiment Dynamics in Crowdfunding Projects. In: Adams, N and Tucker, A and Weston, D, (eds.) Advances in Intelligent Data Analysis XVI: 16th International Symposium, IDA 2017: Proceedings. (pp. pp. 40-51). Springer

Kalaitzis, A; Silva, R; (2013) Flexible sampling of discrete data correlations without the marginal distributions. In: Burges, CJC and Bottou, L and Welling, M and Ghahramani, Z and Weinberger, KQ, (eds.) Advances in Neural Information Processing Systems 26 (NIPS 2013). Neural Information Processing Systems Foundation: Lake Tahoe, NV, USA. Green open access
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Ng, YC; Chilinski, P; Silva, R; (2016) Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages. In: Proceedings of the 29th Conference on Neural Information Processing Systems (NIPS 2016). Neural Information Processing Systems Foundation: Barcelona, Spain. Green open access
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Sanborn, AN; Silva, R; (2009) Belief propagation and locally Bayesian learning. In: Taatgen, N and van Rijn, H, (eds.) (Proceedings) COGSCI 2009. (pp. pp. 389-394).

Silva, R; (2016) Observational-Interventional Priors for Dose-Response Learning. In: Proceedings of the Advances in Neural Information Processing Systems 29 (NIPS 2016). NIPS Proceedings: Barcelona, Spain. Green open access
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Silva, R; (2015) Bayesian Inference in Cumulative Distribution Fields. In: Polpo, A and Louzada, F and Rifo, LLR and Stern, JM and Lauretto, M, (eds.) Interdisciplinary Bayesian Statistics. (pp. pp. 83-95). Springer: Cham, Switzerland.

Silva, R; (2013) A MCMC Approach for Learning the Structure of Gaussian Acyclic Directed Mixed Graphs. In: Giudici, P and Ingrassia, S and Vichi, M, (eds.) Statistical Models for Data Analysis. (pp. pp. 343-352). Springer International Publishing: Switzerland.

Silva, R; (2012) Latent Composite Likelihood Learning for the Structured Canonical Correlation Model. In: Uncertainty in Artificial Intelligence: Proceedings of the Twenty-Eighth Conference (2012). (pp. pp. 765-774). AUAI Press: Corvallis, Oregon, USA. Green open access
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Silva, R; (2011) Thinning Measurement Models and Questionnaire Design. In: Shawe-Taylor, J and Zemel, RS and Bartlett, PL and Pereira, FCN and Weinberger, KQ, (eds.) Advances in Neural Information Processing Systems 24 (NIPS 2011). (pp. 307 - 315). NIPS Proceedings

Silva, R; Blundell, C; Teh, YW; (2011) Mixed cumulative distribution networks. In: Gordon, G and Dunson, D and Dudík, M, (eds.) (Proceedings) AISTATS 2011. (pp. pp. 670-678).

Silva, R; Evans, R; (2014) Causal Inference through a Witness Protection Program. In: Gharamani, Z and Welling, W and Cortes, C and Lawrence, ND and Weinberger, KQ, (eds.) Advances in Neural Information Processing Systems 27 (NIPS 2014). Neural Information Processing Systems Foundation: Montreal, Canada. Green open access
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Silva, R; Ghaharamani, Z; (2009) Factorial mixture of Gaussians and the marginal independence model. In: van Dyk, D and Welling, M, (eds.) (Proceedings) AISTATS 2009. (pp. pp. 520-527).

Silva, R; Ghahramani, Z; Bayesian Inference for Gaussian Mixed Graph Models. In:

Silva, R; Gramacy, R; (2009) MCMC methods for Bayesian mixtures of copulas. In: van Dyk, D and Welling, M, (eds.) (Proceedings) AISTATS 2009. (pp. pp. 512-519).

Silva, R; Gramacy, RB; Gaussian Process Structural Equation Models with Latent Variables. In:

Silva, R; Heller, KA; Ghaharamani, Z; (2007) Analogical reasoning with relational Bayesian sets. In: Meila, M and Shen, X, (eds.) (Proceedings) AISTATS 2007.

Silva, R; Ludermir, T; (2000) Obtaining simplified rules by hybrid learning. In: Langley, P, (ed.) (Proceedings) ICML 2000. (pp. pp. 879-886). Morgan Kauffman: San Francisco, US.

Silva, R; Moody, J; Vanderwaart, J; Glymour, C; (2001) Data filtering for automatic classification of rocks from reflectance spectra. In: Provost, F and Srikant, R, (eds.) (Proceedings) KDD 2001. (pp. pp. 347-352). ACM Press: San Francisco, US.

Silva, R; Scheines, R; (2006) Towards association rules with hidden variables. In: Furnkranz, J and Scheffer, T and Spiliopoulou, M, (eds.) (Proceedings) 10th European Conference on Principle and Practice of Knowledge Discovery in Databases. (pp. pp. 617-624). SPRINGER-VERLAG BERLIN

Silva, R; Scheines, R; (2006) Bayesian learning of measurement and structural models. In: Cohen, W and Moore, A, (eds.) (Proceedings) ICML 2006. ACM Press: Pittsburgh, PA.

Silva, R; Scheines, R; (2005) New d-separation identification results for learning continuous latent variable models. In: De Raedt, L and Wrobel, S, (eds.) (Proceedings) ICML 2005. (pp. pp. 808-815). ACM Press: New York, US.

Silva, R; Scheines, R; Glymour, C; Spirtes, PL; Learning Measurement Models for Unobserved Variables. In:

Silva, R; Wei, C; Ghaharamani, Z; (2007) Hidden common cause relations in relational learning. In: Platt, J and Koller, D and Singer, Y and Roweis, S, (eds.) (Proceedings) NIPS 2007. (pp. pp. 1345-1352). MIT Press: Cambridge, MA.

Silva, R; Zhang, J; Shanahan, J; (2005) Probabilistic workflow mining. In: Bayardo, R and Bennet, K, (eds.) (Proceedings) KDD 2005. (pp. pp. 275-284). ACM Press: San Francisco, US.

Zhang, J; Silva, R; (2011) Discussion of “Learning Equivalence Classes of Acyclic Models with Latent and Selection Variables from Multiple Datasets with Overlapping Variables”. In: Gordon, G and Dunson, D and Dudík, M, (eds.) (Proceedings) AISTATS 2011. (pp. pp. 16-18).

Report

Gramacy, RB; Lee, JH; Silva, R; (2007) On estimating covariances between many assets with histories of highly variable length.

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

Thesis

Parsons, S; (2016) Approximation methods for latent variable models. Doctoral thesis , UCL (University College London). Green open access
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Patent

Shanahan, J; Silva, R; Zhang, J; (2007) Method and apparatus for probabilistic workflow mining. US 2007/0055558 A1.

This list was generated on Sun Nov 12 07:15:54 2017 GMT.