D. M. Roy

      Learning annotated hierarchies from relational data (pdf) (bibtex)

      D. M. Roy, C. Kemp, V. K. Mansinghka, J. B. Tenenbaum:
      Advances in Neural Information Processing Systems 19
      #stochastic blockmodels, #annotated hierarchies, #hierachical

      When are probabilistic programs probably computationally tractable? (pdf) (bibtex)

      C. E. Freer, V. K. Mansinghka, D. M. Roy:
      NIPS Workshop on Monte Carlo Methods for Modern Applications
      #probabilistic programming

      Church: a language for generative models (pdf) (bibtex)

      N. D. Goodman, V. K. Mansinghka, D. M. Roy, K. Bonawitz, J. B. Tenenbaum:
      Uncertainty in Artificial Intelligence 2008
      #church, #probabilistic programming, #generative models

      AClass: An online algorithm for generative classification (pdf) (bibtex)

      V. K. Mansinghka, D. M. Roy, R. Rifkin, J. B. Tenenbaum:
      Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS07)
      #classification, #dirichlet process mixture, #particle filter

      Discovering syntactic hierarchies (pdf) (bibtex)

      V. Savova, D. M. Roy, L. Schmidt, J. B. Tenenbaum:
      Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society
      #annotated hierarchies, #stochastic blockmodels

      Exact and approximate sampling by systematic stochastic search (pdf) (bibtex)

      V. K. Mansinghka, D. M. Roy, E. Jonas, J. B. Tenenbaum:
      AISTATS 2009

      Towards common-sense reasoning via conditional simulation: Legacies of Turing in Artificial Intelligence (web) (bibtex)

      C. E. Freer, D. M. Roy, J. B. Tenenbaum:
      Turing's Legacy (ASL Lecture Notes in Logic)
      #probabilistic programming, #artificial intelligence, #reasoning, #decision making, #structure learning

      Computable exchangeable sequences have computable de Finetti measures (pdf) (doi) (bibtex)

      C. E. Freer, D. M. Roy:
      Computability in Europe (CiE)
      #de finetti, #exchangeability, #computable probability theory, #probabilistic programming, #mutation

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