Publications

    Probabilistic Programming

  • D. Wingate, A. Stuhlmueller, N. D. Goodman:
    Lightweight Implementations of Probabilistic Programming Languages Via Transformational Compilation (pdf) (bibtex)
    Proceedings of the 14th international conference on Artificial Intelligence and Statistics
    #probabilistic programming, #mcmc, #inference
    @article{D.Wingate:2011:f0689,
    author = {D. Wingate and A. Stuhlmueller and N. D. Goodman},
    journal = {Proceedings of the 14th international conference on Artificial Intelligence and Statistics},
    title = {Lightweight Implementations of Probabilistic Programming Languages Via Transformational Compilation},
    year = {2011},
    keywords = {probabilistic programming, mcmc, inference},
    doi = {},
    url = {http://www.mit.edu/~ast/papers/lightweight-mcmc-aistats2011.pdf}
    }
  • C. E. Freer, V. K. Mansinghka, D. M. Roy:
    When are probabilistic programs probably computationally tractable? (pdf) (bibtex)
    NIPS Workshop on Monte Carlo Methods for Modern Applications
    #probabilistic programming
    @article{C.E.Freer:2010:f8bd8,
    author = {C. E. Freer and V. K. Mansinghka and D. M. Roy},
    journal = {NIPS Workshop on Monte Carlo Methods for Modern Applications},
    title = {When are probabilistic programs probably computationally tractable? },
    year = {2010},
    keywords = {probabilistic programming},
    doi = {},
    url = {http://danroy.org/papers/FreerManRoy-NIPSMC-2010.pdf}
    }
  • N. D. Goodman, V. K. Mansinghka, D. M. Roy, K. Bonawitz, J. B. Tenenbaum:
    Church: a language for generative models (pdf) (bibtex)
    Uncertainty in Artificial Intelligence 2008
    #church, #probabilistic programming, #generative models
    @article{N.D.Goodman:2008:f5d3f,
    author = {N. D. Goodman and V. K. Mansinghka and D. M. Roy and K. Bonawitz and J. B. Tenenbaum},
    journal = {Uncertainty in Artificial Intelligence 2008},
    title = {Church: a language for generative models},
    year = {2008},
    keywords = {church, probabilistic programming, generative models},
    doi = {},
    url = {http://danroy.org/papers/church_GooManRoyBonTen-UAI-2008.pdf}
    }
  • T. Gerstenberg, N.D. Goodman:
    Ping Pong in Church: Productive use of concepts in human probabilistic inference (pdf) (bibtex)
    Proceedings of the 34th Annual Conference of the Cognitive Science Society
    #inference, #reasoning, #causality, #language of thought, #probabilistic programming
    @article{T.Gerstenberg:2011:ac923,
    author = {T. Gerstenberg and N.D. Goodman},
    journal = {Proceedings of the 34th Annual Conference of the Cognitive Science Society},
    title = {Ping Pong in Church: Productive use of concepts in human probabilistic inference},
    year = {2011},
    keywords = {inference, reasoning, causality, language of thought, probabilistic programming},
    doi = {},
    url = {http://web.mit.edu/tger/www/papers/Ping%20Pong%20in%20Church%20Productive%20use%20of%20concepts%20in%20human%20probabilistic%20inference,%20Gerstenberg,%20Goodman,%202012.pdf}
    }
  • C. E. Freer, D. M. Roy, J. B. Tenenbaum:
    Towards common-sense reasoning via conditional simulation: Legacies of Turing in Artificial Intelligence (web) (bibtex)
    Turing’s Legacy (ASL Lecture Notes in Logic)
    #probabilistic programming, #artificial intelligence, #reasoning, #decision making, #structure learning
    @article{C.E.Freer:2012:ed3e0,
    author = {C. E. Freer and D. M. Roy and J. B. Tenenbaum},
    journal = {Turing’s Legacy (ASL Lecture Notes in Logic)},
    title = {Towards common-sense reasoning via conditional simulation: Legacies of Turing in Artificial Intelligence},
    year = {2012},
    keywords = {probabilistic programming, artificial intelligence, reasoning, decision making, structure learning},
    doi = {},
    url = {http://arxiv.org/abs/1212.4799}
    }
  • C. E. Freer, D. M. Roy:
    Computable exchangeable sequences have computable de Finetti measures (pdf) (doi) (bibtex)
    Computability in Europe (CiE)
    #de finetti, #exchangeability, #computable probability theory, #probabilistic programming, #mutation
    @article{C.E.Freer:2009:e8caa,
    author = {C. E. Freer and D. M. Roy},
    journal = {Computability in Europe (CiE)},
    title = {Computable exchangeable sequences have computable de Finetti measures},
    year = {2009},
    keywords = {de finetti, exchangeability, computable probability theory, probabilistic programming, mutation},
    doi = {10.1007/978-3-642-03073-4_23},
    url = {http://danroy.org/papers/FreerRoy-CIE-2009.pdf}
    }
  • A. Stuhlmueller, N. D. Goodman:
    Reasoning about Reasoning by Nested Conditioning: Modeling Theory of Mind with Probabilistic Programs (pdf) (bibtex)
    Journal of Cognitive Systems Research
    #theory of mind, #probabilistic programming
    @article{A.Stuhlmueller:2013:55d25,
    author = {A. Stuhlmueller and N. D. Goodman},
    journal = {Journal of Cognitive Systems Research},
    title = {Reasoning about Reasoning by Nested Conditioning: Modeling Theory of Mind with Probabilistic Programs},
    year = {2013},
    keywords = {theory of mind, probabilistic programming},
    doi = {},
    url = {http://www.mit.edu/~ast/papers/nested-conditioning-cogsys2013.pdf}
    }
  • A. Stuhlmueller, N. D. Goodman:
    A Dynamic Programming Algorithm for Inference in Recursive Probabilistic Programs (web) (bibtex)
    Second Statistical Relational AI workshop at UAI 2012 (StaRAI-12)
    #dynamic programming, #inference, #probabilistic programming
    @article{A.Stuhlmueller:2012:da21e,
    author = {A. Stuhlmueller and N. D. Goodman},
    journal = {Second Statistical Relational AI workshop at UAI 2012 (StaRAI-12)},
    title = {A Dynamic Programming Algorithm for Inference in Recursive Probabilistic Programs},
    year = {2012},
    keywords = {dynamic programming, inference, probabilistic programming},
    doi = {},
    url = {http://arxiv.org/abs/1206.3555}
    }
  • I. Hwang, A. Stuhlmueller, N. D. Goodman:
    Inducing Probabilistic Programs by Bayesian Program Merging (web) (bibtex)
    Technical Report. arXiv:1110.5667 [cs.AI]
    #probabilistic programming, #program induction
    @article{I.Hwang:2011:0c73e,
    author = {I. Hwang and A. Stuhlmueller and N. D. Goodman},
    journal = {Technical Report. arXiv:1110.5667 [cs.AI]},
    title = {Inducing Probabilistic Programs by Bayesian Program Merging},
    year = {2011},
    keywords = {probabilistic programming, program induction},
    doi = {},
    url = {http://arxiv.org/abs/1110.5667}
    }
  • D. Wingate, N. D. Goodman, A. Stuhlmueller, J. M. Siskind:
    Nonstandard Interpretations of Probabilistic Programs for Efficient Inference (pdf) (bibtex)
    Advances in Neural Information Processing Systems (NIPS) 24
    #probabilistic programming, #inference
    @article{D.Wingate:2011:ac593,
    author = {D. Wingate and N. D. Goodman and A. Stuhlmueller and J. M. Siskind},
    journal = {Advances in Neural Information Processing Systems (NIPS) 24},
    title = {Nonstandard Interpretations of Probabilistic Programs for Efficient Inference},
    year = {2011},
    keywords = {probabilistic programming, inference},
    doi = {},
    url = {http://www.mit.edu/~ast/papers/nonstandard-interpretations-nips2011.pdf}
    }
  • Vikash Mansinghka*, Tejas Kulkarni*, Yura Perov, Josh Tenenbaum:
    Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs (pdf) (bibtex)
    NIPS 2013
    #probabilistic programming, #computational vision, #inverse graphics, #mcmc
    @article{VikashMansinghka*:2013:ea642,
    author = {Vikash Mansinghka* and Tejas Kulkarni* and Yura Perov and Josh Tenenbaum},
    journal = {NIPS 2013},
    title = {Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs},
    year = {2013},
    keywords = {probabilistic programming, computational vision, inverse graphics, mcmc},
    doi = {},
    url = {http://arxiv.org/pdf/1307.0060v1.pdf}
    }
  • Tejas D Kulkarni, Pushmeet Kohli, Joshua B Tenenbaum, Vikash Mansinghka:
    Picture : A Probabilistic Programming Language for Scene Perception (pdf) (doi) (bibtex)
    Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
    #analysis-by-synthesis, #inference, #deep learning, #inverse vision, #3d vision, #probabilistic programming
    @article{TejasDKulkarni:2015:cf27d,
    author = {Tejas D Kulkarni and Pushmeet Kohli and Joshua B Tenenbaum and Vikash Mansinghka},
    journal = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
    title = {Picture : A Probabilistic Programming Language for Scene Perception},
    year = {2015},
    keywords = {analysis-by-synthesis, inference, deep learning, inverse vision, 3d vision, probabilistic programming},
    doi = {10.1109/CVPR.2015.7299068},
    url = {http://openaccess.thecvf.com/content_cvpr_2015/papers/Kulkarni_Picture_A_Probabilistic_2015_CVPR_paper.pdf}
    }

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