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Conference

  1. Rainforth, T., Cornish, R., Yang, H., Warrington, A., & Wood, F. (2018). On Nesting Monte Carlo Estimators. In ICML. BIB
    @inproceedings{rainforth2018nesting,
      title = {On Nesting Monte Carlo Estimators},
      author = {Rainforth, Tom and Cornish, Robert and Yang, Hongseok and Warrington, Andrew and Wood, Frank},
      booktitle = {ICML},
      year = {2018}
    }
    
  2. Rainforth, T., Kosiorek, A. R., Le, T. A., Maddison, C. J., Igl, M., Wood, F., & Teh, Y. W. (2018). Tighter variational bounds are not necessarily better. In ICML. BIB
    @inproceedings{rainforth2018tighter,
      title = {Tighter variational bounds are not necessarily better},
      author = {Rainforth, Tom and Kosiorek, Adam R and Le, Tuan Anh and Maddison, Chris J and Igl, Maximilian and Wood, Frank and Teh, Yee Whye},
      booktitle = {ICML},
      year = {2018}
    }
    
  3. Igl, M., Zintgraf, L., Le, T. A., Wood, F., & Whiteson, S. (2018). Deep Variational Reinforcement Learning for POMDPs. In ICML. BIB
    @inproceedings{igl2018deep,
      title = {Deep Variational Reinforcement Learning for POMDPs},
      author = {Igl, Maximilian and Zintgraf, Luisa and Le, Tuan Anh and Wood, Frank and Whiteson, Shimon},
      booktitle = {ICML},
      year = {2018}
    }
    
  4. Siddarth, N., Paige, B., Desmaison, A., van de Meent, J. W., Wood, F., Goodman, N., … Torr, P. H. S. (2017). Learning Disentangled Representations with Semi-Supervised Deep Generative Models. In NIPS. BIB PDF
    @inproceedings{iffsidnips2017,
      title = {Learning Disentangled Representations with Semi-Supervised Deep Generative Models},
      author = {Siddarth, N. and Paige, B. and Desmaison, A. and van~de~Meent, J.W. and Wood, F. and Goodman, N. and Kohli, P. and Torr, P.H.S},
      booktitle = {NIPS},
      year = {2017}
    }
    
  5. Le, T. A., Baydin, A. G., Zinkov, R., & Wood, F. (2017). Using Synthetic Data to Train Neural Networks is Model-Based Reasoning. In 30th International Joint Conference on Neural Networks, May 14–19, 2017, Anchorage, AK, USA (To Appear). BIB PDF
    @inproceedings{le2016synthetic,
      author = {Le, Tuan Anh and Baydin, Atılım Güneş and Zinkov, Robert and Wood, Frank},
      booktitle = {30th International Joint Conference on Neural Networks, May 14--19, 2017, Anchorage, AK, USA (To Appear)},
      title = {Using Synthetic Data to Train Neural Networks is Model-Based Reasoning},
      year = {2017}
    }
    
  6. Le, T. A., Baydin, A. G., & Wood, F. (2017). Inference Compilation and Universal Probabilistic Programming. In 20th International Conference on Artificial Intelligence and Statistics, April 20–22, 2017, Fort Lauderdale, FL, USA (To Appear). BIB PDF
    @inproceedings{le2016inference,
      author = {Le, Tuan Anh and Baydin, Atılım Güneş and Wood, Frank},
      booktitle = {20th International Conference on Artificial Intelligence and Statistics, April 20--22, 2017, Fort Lauderdale, FL, USA (To Appear)},
      title = {Inference {C}ompilation and {U}niversal {P}robabilistic {P}rogramming},
      year = {2017},
      file = {../assets/pdf/le2016inference.pdf},
      link = {https://arxiv.org/abs/1610.09900}
    }
    
  7. Rainforth, T., Le, T. A., van de Meent, J.-W., Osborne, M. A., & Wood, F. (2016). Bayesian Optimization for Probabilistic Programs. In Advances in Neural Information Processing Systems (NIPS) (pp. 280–288). BIB PDF
    @inproceedings{rainforth-nips-2016,
      title = {{B}ayesian {O}ptimization for {P}robabilistic {P}rograms},
      author = {Rainforth, Tom and Le, Tuan Anh and van de Meent, Jan-Willem and Osborne, Michael A and Wood, Frank},
      booktitle = {Advances in Neural Information Processing Systems (NIPS)},
      year = {2016},
      pages = {280--288}
    }
    
  8. Dhir, N., Perov, Y., & Wood, F. (2016). Nonparametric Bayesian Models for Unsupervised Activity Recognition and Tracking. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016). BIB PDF
    @inproceedings{Dhir-IROS-2016,
      author = {Dhir, Neil and Perov, Yura and Wood, Frank},
      title = {Nonparametric Bayesian Models for Unsupervised Activity Recognition and Tracking},
      booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016)},
      year = {2016}
    }
    
  9. Staton, S., Yang, H., Heunen, C., Kammar, O., & Wood, F. (2016). Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints. In Thirty-First Annual ACM/IEEE Symposium on Logic In Computer Science. BIB PDF
    @inproceedings{staton2016semanticslics,
      title = {Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints},
      author = {Staton, S. and Yang, H. and Heunen, C. and Kammar, O. and Wood, F.},
      booktitle = {Thirty-First Annual ACM/IEEE Symposium on Logic In Computer Science},
      year = {2016}
    }
    
  10. Perov, Y., & Wood, F. (2016). Automatic Sampler Discovery via Probabilistic Programming and Approximate Bayesian Computation. In Artificial General Intelligence (pp. 262–273). BIB PDF
    @inproceedings{perov-agi-2016,
      author = {Perov, Y. and Wood, F.},
      booktitle = {Artificial General Intelligence},
      title = {Automatic Sampler Discovery via Probabilistic Programming and Approximate {B}ayesian Computation},
      pages = {262--273},
      year = {2016}
    }
    
  11. Paige, B., Sejdinovic, D., & Wood, F. (2016). Super-sampling with Reservoir. In Proceedings of the 32nd Annual Conference on Uncertainty in Artificial Intelligence (UAI-2016). AUAI Press. BIB PDF
    @inproceedings{paige2016supersampling,
      author = {Paige, Brooks and Sejdinovic, Dino and Wood, Frank},
      title = {Super-sampling with Reservoir},
      booktitle = {Proceedings of the 32nd Annual Conference on Uncertainty in Artificial Intelligence (UAI-2016)},
      publisher = {{AUAI Press}},
      year = {2016}
    }
    
  12. Paige, B., & Wood, F. (2016). Inference Networks for Sequential Monte Carlo in Graphical Models. In Proceedings of the 33rd International Conference on Machine Learning (Vol. 48). BIB PDF
    @inproceedings{paige2016inference,
      title = {Inference Networks for Sequential {M}onte {C}arlo in Graphical Models},
      author = {Paige, B. and Wood, F.},
      booktitle = {Proceedings of the 33rd International Conference on Machine Learning},
      series = {JMLR},
      volume = {48},
      year = {2016}
    }
    
  13. Rainforth, T., Naesseth, C. A., Lindsten, F., Paige, B., van de Meent, J. W., Doucet, A., & Wood, F. (2016). Interacting Particle Markov Chain Monte Carlo. In Proceedings of the 33rd International Conference on Machine Learning (Vol. 48). BIB PDF
    @inproceedings{rainforth2016interacting,
      title = {Interacting Particle {M}arkov Chain {M}onte {C}arlo},
      author = {Rainforth, T. and Naesseth, C.A. and Lindsten, F. and Paige, B. and van de Meent, J.W. and Doucet, A. and Wood, F.},
      booktitle = {Proceedings of the 33rd International Conference on Machine Learning},
      series = {JMLR},
      volume = {48},
      year = {2016}
    }
    
  14. van de Meent, J. W., Tolpin, D., Paige, B., & Wood, F. (2016). Black-Box Policy Search with Probabilistic Programs. In Proceedings of the Nineteenth International Conference on Artificial Intelligence and Statistics (pp. 1195–1204). BIB PDF
    @inproceedings{vandemeent16,
      author = {van de Meent, J.~W. and Tolpin, D. and Paige, B. and Wood, F.},
      booktitle = {Proceedings of the Nineteenth International Conference on Artificial Intelligence and Statistics},
      title = {Black-Box Policy Search with Probabilistic Programs},
      pages = {1195--1204},
      year = {2016}
    }
    
  15. Tolpin, D., van de Meent, J.-W., & Paige, F., Brooks Wood. (2015). Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs. In ECML PKDD 2015. BIB PDF
    @inproceedings{Tolpin-ECMLPKDD-2015,
      author = {Tolpin, David and van de Meent, Jan-Willem and Paige, Brooks Wood, Frank},
      booktitle = {ECML PKDD 2015},
      title = {{Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs}},
      year = {2015}
    }
    
  16. Tolpin, D., & Wood, F. (2015). Maximum a Posteriori Estimation by Search in Probabilistic Programs. In Eighth Annual Symposium on Combinatorial Search (pp. 201–205). BIB PDF
    @inproceedings{Tolpin-SOCS-2015,
      title = {Maximum a Posteriori Estimation by Search in Probabilistic Programs},
      author = {Tolpin, David and Wood, Frank},
      booktitle = {Eighth Annual Symposium on Combinatorial Search},
      pages = {201-205},
      year = {2015}
    }
    
  17. Tolpin, D., van de Meent, J.-W., & Wood, F. (2015). Probabilistic Programming in Anglican. In A. Bifet, M. May, B. Zadrozny, R. Gavalda, D. Pedreschi, F. Bonchi, … M. Spiliopoulou (Eds.), Machine Learning and Knowledge Discovery in Databases (Vol. 9286, pp. 308–311). Springer International Publishing. https://doi.org/10.1007/978-3-319-23461-8_36 BIB PDF
    @incollection{Tolpin-ECML-Anglican-DemoTrack,
      year = {2015},
      isbn = {978-3-319-23460-1},
      booktitle = {Machine Learning and Knowledge Discovery in Databases},
      volume = {9286},
      series = {Lecture Notes in Computer Science},
      editor = {Bifet, Albert and May, Michael and Zadrozny, Bianca and Gavalda, Ricard and Pedreschi, Dino and Bonchi, Francesco and Cardoso, Jaime and Spiliopoulou, Myra},
      doi = {10.1007/978-3-319-23461-8_36},
      title = {Probabilistic Programming in Anglican},
      url = {http://dx.doi.org/10.1007/978-3-319-23461-8_36},
      publisher = {Springer International Publishing},
      keywords = {Probabilistic programming},
      author = {Tolpin, David and van de Meent, Jan-Willem and Wood, Frank},
      pages = {308-311},
      language = {English}
    }
    
  18. van de Meent, J.-W., Yang, H., Mansinghka, V., & Wood, F. (2015). Particle Gibbs with Ancestor Sampling for Probabilistic Programs. In Artificial Intelligence and Statistics. BIB PDF
    @inproceedings{vandeMeent-AISTATS-2015,
      archiveprefix = {arXiv},
      arxivid = {1501.06769},
      author = {van de Meent, Jan-Willem and Yang, Hongseok and Mansinghka, Vikash and Wood, Frank},
      booktitle = {Artificial Intelligence and Statistics},
      eprint = {1501.06769},
      title = {{Particle Gibbs with Ancestor Sampling for Probabilistic Programs}},
      year = {2015}
    }
    
  19. Paige, B., Wood, F., Doucet, A., & Teh, Y. W. (2014). Asynchronous Anytime Sequential Monte Carlo. In Advances in Neural Information Processing Systems (pp. 3410–3418). BIB PDF
    @inproceedings{Paige-NIPS-2014,
      author = {Paige, B. and Wood, F. and Doucet, A. and Teh, Y.W.},
      booktitle = {Advances in Neural Information Processing Systems},
      title = {Asynchronous Anytime Sequential Monte Carlo},
      pages = {3410--3418},
      year = {2014}
    }
    
  20. Dhir, N., & Wood, F. (2014). Improved Activity Recognition via Kalman Smoothing and Multiclass Linear Discriminant Analysis. In Proceedings of the 36th IEEE Conference on Engineering in Medicine and Biological Systems. BIB PDF
    @inproceedings{Dhir-EMBS-2014,
      author = {Dhir, N. and Wood, F.},
      booktitle = {Proceedings of the 36th IEEE Conference on Engineering in Medicine and Biological Systems},
      title = {Improved Activity Recognition via Kalman Smoothing and
      Multiclass Linear Discriminant Analysis},
      year = {2014}
    }
    
  21. Paige, B., & Wood, F. (2014). A Compilation Target for Probabilistic Programming Languages. In JMLR; ICML 2014 (pp. 1935–1943). BIB PDF
    @inproceedings{Paige-ICML-2014,
      title = {A Compilation Target for Probabilistic Programming Languages},
      author = {Paige, Brooks and Wood, Frank},
      booktitle = {JMLR; ICML 2014},
      pages = {1935--1943},
      year = {2014}
    }
    
  22. Wood, F., van de Meent, J. W., & Mansinghka, V. (2014). A New Approach to Probabilistic Programming Inference. In Artificial Intelligence and Statistics (pp. 1024–1032). BIB PDF
    @inproceedings{Wood-AISTATS-2014,
      author = {Wood, F. and van de Meent, J. W. and Mansinghka, V.},
      booktitle = {Artificial Intelligence and Statistics},
      title = {A New Approach to Probabilistic Programming Inference},
      pages = {1024--1032},
      year = {2014}
    }
    
  23. Neiswanger, W., Wood, F., & Xing, E. (2014). The Dependent Dirichlet Process Mixture of Objects for Detection-free Tracking and Object Modeling. In Artificial Intelligence and Statistics (pp. 660–668). BIB PDF
    @inproceedings{Neiswanger-AISTATS-2014,
      author = {Neiswanger, W. and Wood, F. and Xing, E.},
      booktitle = {Artificial Intelligence and Statistics},
      title = { The Dependent {D}irichlet Process Mixture of Objects for Detection-free Tracking and Object Modeling},
      pages = {660--668},
      year = {2014}
    }
    
  24. Elsner, M., Goldwater, S., Feldman, N., & Wood, F. (2013). A Joint Learning Model of Word Segmentation, Lexical Acquisition, and Phonetic Variability. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (pp. 42–54). Seattle, Washington, USA: Association for Computational Linguistics. BIB PDF
    @inproceedings{Elsner-EMNLP-2013,
      author = {Elsner, M. and Goldwater, S. and Feldman, N. and Wood, F.},
      title = {A Joint Learning Model of Word Segmentation, Lexical Acquisition, and Phonetic Variability},
      booktitle = {Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing},
      month = oct,
      year = {2013},
      address = {Seattle, Washington, USA},
      publisher = {Association for Computational Linguistics},
      pages = {42--54}
    }
    
  25. van de Meent, J. W., Bronson, J. E., Jr., R. L. G., Wood, F., & Wiggins, C. H. (2013). Learning biochemical kinetic models from single-molecule data with hierarchically-coupled hidden Markov models. In International Conference on Machine Learning (pp. 361–369). BIB PDF
    @inproceedings{vandeMeent-ICML-2013,
      author = {van de Meent, J.W. and Bronson, J. E. and Jr., R. L. Gonzalez and Wood, F. and Wiggins, C. H.},
      booktitle = {International Conference on Machine Learning},
      title = {Learning biochemical kinetic models from single-molecule data with hierarchically-coupled hidden {M}arkov models},
      year = {2013},
      pages = {361--369}
    }
    
  26. Smith, C., Wood, F., & Paninski, L. (2012). Low rank continuous-space graphical models. In Artificial Intelligence and Statistics (pp. 1064–1072). BIB PDF
    @inproceedings{Smith-AISTATS-2012,
      author = {Smith, C. and Wood, F. and Paninski, L.},
      booktitle = {Artificial Intelligence and Statistics},
      title = { Low rank continuous-space graphical models},
      pages = {1064--1072},
      year = {2012}
    }
    
  27. Perotte, A., Bartlett, N., Elhadad, N., & Wood, F. (2011). Hierarchically Supervised Latent Dirichlet Allocation. In Advances in Neural Information Processing Systems (pp. 2609–2617). BIB PDF
    @inproceedings{Perotte-NIPS-2011,
      author = {Perotte, A. and Bartlett, N. and Elhadad, N. and Wood, F.},
      booktitle = {Advances in Neural Information Processing Systems},
      title = {Hierarchically Supervised Latent {D}irichlet Allocation},
      year = {2011},
      pages = {2609--2617}
    }
    
  28. Bartlett, N., & Wood, F. (2011). Deplump for Streaming Data. In Data Compression Conference (pp. 363–372). BIB PDF
    @inproceedings{Bartlett-DCC-2011,
      author = {Bartlett, N. and Wood, F.},
      booktitle = {Data Compression Conference},
      title = {Deplump for Streaming Data},
      pages = {363--372},
      year = {2011}
    }
    
  29. Pfau, D., Bartlett, N., & Wood, F. (2011). Probabilistic Deterministic Infinite Automata. In Advances in Neural Information Processing Systems (pp. 1930–1938). BIB PDF
    @inproceedings{Pfau-NIPS-2011,
      author = {Pfau, D. and Bartlett, N. and Wood, F.},
      booktitle = {Advances in Neural Information Processing Systems},
      title = {Probabilistic Deterministic Infinite Automata},
      year = {2011},
      pages = {1930--1938}
    }
    
  30. Bartlett, N., Pfau, D., & Wood, F. (2010). Forgetting Counts : Constant Memory Inference for a Dependent Hierarchical Pitman-Yor Process. In Proceedings of the 26th International Conference on Machine Learning (pp. 63–70). BIB PDF
    @inproceedings{Bartlett-ICML-2010,
      author = {Bartlett, N. and Pfau, D. and Wood, F.},
      booktitle = {Proceedings of the 26th International Conference on Machine Learning},
      title = {Forgetting Counts : Constant Memory Inference for a Dependent Hierarchical {P}itman-{Y}or Process},
      year = {2010},
      pages = {63--70}
    }
    
  31. Gasthaus, J., Wood, F., & Teh, Y. W. (2010). Lossless compression based on the Sequence Memoizer . In Data Compression Conference (pp. 337–345). BIB PDF
    @inproceedings{Gasthaus-DCC-2010,
      author = {Gasthaus, J. and Wood, F. and Teh, Y.W.},
      booktitle = {Data Compression Conference},
      pages = {337--345},
      title = {Lossless compression based on the {S}equence {M}emoizer },
      year = {2010}
    }
    
  32. Wood, F., Archambeau, C., Gasthaus, J., James, L., & Teh, Y. W. (2009). A Stochastic Memoizer for Sequence Data . In Proceedings of the 26th International Conference on Machine Learning (pp. 1129–1136). BIB PDF
    @inproceedings{Wood-ICML-2009,
      author = {Wood, F. and Archambeau, C. and Gasthaus, J. and James, L. and Teh, Y.W.},
      booktitle = {Proceedings of the 26th International Conference on Machine Learning},
      pages = {1129--1136},
      title = {A Stochastic Memoizer for Sequence Data },
      year = {2009}
    }
    
  33. Wood, F., & Teh, Y. W. (2009). A Hierarchical Nonparametric Bayesian Approach to Statistical Language Model Domain Adaptation. In Artificial Intelligence and Statistics (pp. 607–614). BIB PDF
    @inproceedings{Wood-AISTATS-2009,
      author = {Wood, F. and Teh, Y.W.},
      booktitle = {Artificial Intelligence and Statistics},
      pages = {607--614},
      title = { A Hierarchical Nonparametric {B}ayesian Approach to Statistical Language Model Domain Adaptation},
      year = {2009}
    }
    
  34. Gasthaus, J., Wood, F., Görür, D., & Teh, Y. W. (2009). Dependent Dirichlet Process Spike Sorting. In Advances in Neural Information Processing Systems (pp. 497–504). BIB PDF
    @inproceedings{Gasthaus-NIPS-2009,
      author = {Gasthaus, J. and Wood, F. and G\"{o}r\"{u}r, D. and Teh, Y.W.},
      booktitle = {Advances in Neural Information Processing Systems},
      pages = {497--504},
      title = {Dependent {D}irichlet Process Spike Sorting},
      year = {2009}
    }
    
  35. Berkes, P., Pillow, J. W., & Wood, F. (2009). Characterizing neural dependencies with Poisson copula models. In Advances in Neural Information Processing Systems (pp. 129–136). BIB PDF
    @inproceedings{Berkes-NIPS-2009,
      author = {Berkes, P. and Pillow, J.W. and Wood, F.},
      booktitle = {Advances in Neural Information Processing Systems},
      pages = {129 -- 136},
      title = {Characterizing neural dependencies with {P}oisson copula models},
      year = {2009}
    }
    
  36. Wood, F., & Griffiths, T. L. (2006). Particle Filtering for Non-Parametric Bayesian Matrix Factorization. In Advances in Neural Information Processing Systems (pp. 1513–1520). BIB PDF
    @inproceedings{Wood-NIPS-2006,
      author = {Wood, F. and Griffiths, T. L.},
      booktitle = {Advances in Neural Information Processing Systems},
      pages = {1513--1520},
      title = {Particle Filtering for Non-Parametric {B}ayesian Matrix Factorization},
      year = {2006}
    }
    
  37. Wood, F., Goldwater, S., & Black, M. J. (2006). A Non-Parametric Bayesian Approach to Spike Sorting. In Proceedings of the 28th IEEE Conference on Engineering in Medicine and Biological Systems (pp. 1165–1169). BIB PDF
    @inproceedings{Wood-EMBS-2006,
      author = {Wood, F. and Goldwater, S. and Black, M. J.},
      booktitle = {Proceedings of the 28th IEEE Conference on Engineering in Medicine and Biological Systems},
      pages = {1165--1169},
      title = {A Non-Parametric {B}ayesian Approach to Spike Sorting},
      year = {2006}
    }
    
  38. Wood, F., Griffiths, T. L., & Ghahramani, Z. (2006). A Non-Parametric Bayesian Method for Inferring Hidden Causes. In Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence (pp. 536–543). BIB PDF
    @inproceedings{Wood-UAI-2006,
      author = {Wood, F. and Griffiths, T. L. and Ghahramani, Z.},
      booktitle = {Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence },
      pages = {536--543},
      title = {A Non-Parametric {B}ayesian Method for Inferring Hidden Causes},
      year = {2006}
    }
    
  39. Kim, S. P., Wood, F., & Black, M. J. (2006). Statistical Analysis of the Non-stationarity of Neural Population Codes. In The First IEEE / RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (pp. 259–299). BIB PDF
    @inproceedings{Kim-BioRob-2006,
      author = {Kim, S. P. and Wood, F. and Black, M. J.},
      booktitle = {The First IEEE / RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics},
      pages = {259--299},
      title = {Statistical Analysis of the Non-stationarity of Neural Population Codes},
      year = {2006}
    }
    
  40. Wood, F., Roth, S., & Black, M. J. (2005). Modeling neural population spiking activity with Gibbs distributions. In Advances in Neural Information Processing Systems (pp. 1527–1544). BIB PDF
    @inproceedings{Wood-NIPS-2005,
      author = {Wood, F. and Roth, S. and Black, M. J.},
      booktitle = {Advances in Neural Information Processing Systems},
      pages = {1527--1544},
      title = {Modeling neural population spiking activity with {G}ibbs distributions},
      year = {2005}
    }
    
  41. Wood, F., Prabhat, Donoghue, J. P., & Black, M. J. (2005). Inferring Attentional State and Kinematics from Motor Cortical Firing Rates. In Proceedings of the 27th IEEE Conference on Engineering in Medicine and Biological Systems (pp. 149–152). BIB PDF
    @inproceedings{Wood-EMBS-2005,
      author = {Wood, F. and Prabhat and Donoghue, J. P. and Black, M. J.},
      booktitle = {Proceedings of the 27th IEEE Conference on Engineering in Medicine and Biological Systems},
      pages = {149--152},
      title = {Inferring Attentional State and Kinematics from Motor Cortical Firing Rates},
      year = {2005}
    }
    
  42. Wood, F., Fellows, M., Donoghue, J. P., & Black, M. J. (2004). Automatic Spike Sorting for Neural Decoding. In Proceedings of the 27th IEEE Conference on Engineering in Medicine and Biological Systems (pp. 4126–4129). BIB PDF
    @inproceedings{Wood-EMBS-2004,
      author = {Wood, F. and Fellows, M. and Donoghue, J. P. and Black, M. J.},
      booktitle = {Proceedings of the 27th IEEE Conference on Engineering in Medicine and Biological Systems},
      pages = {4126--4129},
      title = {Automatic Spike Sorting for Neural Decoding},
      year = {2004}
    }
    

Journal

  1. Caron, F., Neiswanger, W., Wood, F., Doucet, A., & Davy, M. (2016). Generalized Pólya Urn for Time-Varying Pitman-Yor Processes. JMLR, to appear. BIB
    @article{Caron-2014-JMLR,
      title = {Generalized {P}\'olya Urn for Time-Varying {P}itman-{Y}or Processes},
      author = {Caron, Francois and Neiswanger, Willie and Wood, Frank and Doucet, Arnaud and Davy, Manuel},
      journal = {JMLR},
      pages = {to appear},
      year = {2016}
    }
    
  2. Perotte, A., Pivovarov, R., Natarajan, K., Weiskopf, N., Wood, F., & Elhadad, N. (2014). Diagnosis code assignment: models and evaluation metrics. Journal of the American Medical Informatics Association, 21(2), 231–237. BIB PDF
    @article{Perotte-JAMIA-2013,
      title = {Diagnosis code assignment: models and evaluation metrics},
      author = {Perotte, Adler and Pivovarov, Rimma and Natarajan, Karthik and Weiskopf, Nicole and Wood, Frank and Elhadad, No{\'e}mie},
      journal = {Journal of the American Medical Informatics Association},
      volume = {21},
      number = {2},
      pages = {231--237},
      year = {2014},
      publisher = {BMJ Publishing Group Ltd}
    }
    
  3. Doshi-Velez, F., Pfau, D., Wood, F., & Roy, N. (2013). Bayesian Nonparametric Methods for Partially-Observable Reinforcement Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 99, 1. BIB PDF
    @article{Doshi-Velez-TPAMI-2013,
      author = {Doshi-Velez, F. and Pfau, D. and Wood, F. and Roy, N.},
      title = {Bayesian Nonparametric Methods for Partially-Observable Reinforcement Learning},
      journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
      volume = {99},
      year = {2013},
      pages = {1},
      publisher = {IEEE Computer Society}
    }
    
  4. Dewar, M., Wiggins, C., & Wood, F. (2012). Inference in Hidden Markov Models with Explicit State Duration Distributions. Signal Processing Letters, IEEE, 19(4), 235–238. BIB PDF
    @article{Dewar-IEEE-2012,
      title = {Inference in Hidden {M}arkov Models with Explicit State Duration Distributions},
      author = {Dewar, M. and Wiggins, C. and Wood, F.},
      journal = {Signal Processing Letters, IEEE},
      volume = {19},
      number = {4},
      pages = {235--238},
      year = {2012}
    }
    
  5. Wood, F., Gasthaus, J., Archambeau, C., James, L., & Teh, Y. W. (2011). The Sequence Memoizer. Communications of the ACM, 54(2), 91–98. BIB PDF
    @article{Wood-CACM-2011,
      author = {Wood, F. and Gasthaus, J. and Archambeau, C. and James, L. and Teh, Y.W.},
      title = {The Sequence Memoizer},
      year = {2011},
      volume = {54},
      number = {2},
      pages = {91--98},
      journal = {Communications of the ACM},
      publisher = {ACM Press}
    }
    
  6. Wood, F., & Black, M. J. (2008). A Non-parametric Bayesian alternative to spike sorting. Journal of Neuroscience Methods, 173, 1–12. BIB PDF
    @article{Wood-JNM-2008,
      author = {Wood, F. and Black, M. J.},
      journal = {Journal of Neuroscience Methods},
      pages = {1--12},
      title = {A Non-parametric {B}ayesian alternative to spike sorting},
      volume = {173},
      year = {2008}
    }
    
  7. Grollman, D. H., Jenkins, O. C., & Wood, F. (2006). Discovering natural kinds of robot sensory experiences in unstructured environments. Journal of Field Robotics, 23, 1077–1089. BIB PDF
    @article{Grollman-JFR-2006,
      author = {Grollman, D. H. and Jenkins, O. C. and Wood, F.},
      journal = {Journal of Field Robotics},
      pages = {1077--1089},
      title = {Discovering natural kinds of robot sensory experiences in unstructured environments},
      volume = {23},
      year = {2006}
    }
    
  8. Wood, F., Fellows, M., Vargas-Irwin, C., Black, M. J., & Donoghue, J. P. (2004). On the Variability of Manual Spike Sorting. IEEE Transactions in Biomedical Engineering, 51, 912–918. BIB PDF
    @article{Wood-TBME-2004,
      author = {Wood, F. and Fellows, M. and Vargas-Irwin, C. and Black, M. J. and Donoghue, J. P.},
      journal = {IEEE Transactions in Biomedical Engineering},
      pages = {912-918},
      title = {On the Variability of Manual Spike Sorting},
      volume = {51},
      year = {2004}
    }
    
  9. Wood, F., Brown, D., Amidon, B., Alferness, J., Joseph, B., Gillilan, R. E., & Faerman, C. (1996). Windowing and Telecollaboration for Virtual Reality with Applications to the Study of a Tropical Disease. IEEE Computer Graphics and Applications, 16, 72–78. BIB
    @article{Wood-IEEE-CompGraphics-1996,
      author = {Wood, F. and Brown, D. and Amidon, B. and Alferness, J. and Joseph, B. and Gillilan, R. E. and Faerman, C.},
      journal = {IEEE Computer Graphics and Applications},
      pages = {72--78},
      title = {Windowing and Telecollaboration for Virtual Reality with Applications to the Study of a Tropical Disease},
      volume = {16},
      year = {1996}
    }
    
  10. Gillilan, R. E., & Wood, F. (1995). Visualization, Virtual Reality, and Animation within the Data Flow Model of Computing. Computer Graphics, 29, 55–58. BIB PDF
    @article{Gillilan-CompGraphics-1995,
      author = {Gillilan, R. E. and Wood, F.},
      journal = {Computer Graphics},
      pages = {55--58},
      title = {Visualization, Virtual Reality, and Animation within the Data Flow Model of Computing},
      volume = {29},
      year = {1995}
    }
    

Technical Reports

  1. Warrington, A., & and Wood, F. (2016). Optimizing Neuron Generation Model Parameters for Top-Down Segmentation Regularization. In NIPS Workshop on Connectomics II: Opportunities and Challenges for Machine Learning. BIB
    @inproceedings{WarringtonWood-NEURO-OPT-NIPS-2016,
      author = {Warrington, Andrew and and Wood, Frank},
      booktitle = {NIPS Workshop on Connectomics II: Opportunities and Challenges for Machine Learning},
      title = {Optimizing Neuron Generation Model Parameters for Top-Down Segmentation Regularization},
      year = {2016}
    }
    
  2. Le, T. A., Baydin, A. G., & Wood, F. (2016). Inference Compilation and Universal Probabilistic Programming. ArXiv Preprint ArXiv:1610.09900. BIB PDF
    @article{le2016inference,
      title = {Inference Compilation and Universal Probabilistic Programming},
      author = {Le, Tuan Anh and Baydin, Atılım Güneş and Wood, Frank},
      journal = {arXiv preprint arXiv:1610.09900},
      year = {2016}
    }
    
  3. Paige, B., & Wood, F. (2016). Inference Networks for Sequential Monte Carlo in Graphical Models. ArXiv Preprint ArXiv:1602.06701. BIB
    @article{paige2016inferencenetworks,
      title = {Inference Networks for Sequential Monte Carlo in Graphical Models},
      author = {Paige, Brooks and Wood, Frank},
      journal = {arXiv preprint arXiv:1602.06701},
      year = {2016}
    }
    
  4. Perov, Y. N., Le, T. A., & Wood, F. (2015). Data-driven Sequential Monte Carlo in Probabilistic Programming. ArXiv Preprint ArXiv:1512.04387. BIB
    @article{perov2015data,
      title = {Data-driven Sequential Monte Carlo in Probabilistic Programming},
      author = {Perov, Yura N and Le, Tuan Anh and Wood, Frank},
      journal = {arXiv preprint arXiv:1512.04387},
      year = {2015}
    }
    
  5. Rainforth, T., Naesseth, C. A., Lindsten, F., Paige, B., van de Meent, J.-W., Doucet, A., & Wood, F. (2016). Interacting Particle Markov Chain Monte Carlo. ArXiv Preprint ArXiv:1602.05128. BIB PDF
    @article{rainforth2016interacting,
      title = {Interacting Particle Markov Chain Monte Carlo},
      author = {Rainforth, Tom and Naesseth, Christian A and Lindsten, Fredrik and Paige, Brooks and van de Meent, Jan-Willem and Doucet, Arnaud and Wood, Frank},
      journal = {arXiv preprint arXiv:1602.05128},
      year = {2016}
    }
    
  6. van de Meent, J.-W., Tolpin, D., Paige, B., & Wood, F. (2015). Black-Box Policy Search with Probabilistic Programs. ArXiv (Accepted at AISTATS 2016), 1507.04635. BIB PDF
    @article{vandemeent_arxiv_1507_04635,
      journal = {ArXiv (accepted at AISTATS 2016)},
      archiveprefix = {arXiv},
      arxivid = {1507.04635},
      author = {van de Meent, Jan-Willem and Tolpin, David and Paige, Brooks and Wood, Frank},
      eprint = {1507.04635},
      pages = {1507.04635},
      title = {{Black-Box Policy Search with Probabilistic Programs}},
      year = {2015}
    }
    
  7. Rainforth, T., & Wood, F. (2015). Canonical Correlation Forests. ArXiv Preprint ArXiv:1507.05444. Retrieved from https://bitbucket.org/twgr/ccf BIB PDF
    @article{Rainforth-2015-CCF-arXiv,
      title = {Canonical Correlation Forests},
      url = {https://bitbucket.org/twgr/ccf},
      author = {Rainforth, Tom and Wood, Frank},
      journal = {arXiv preprint arXiv:1507.05444},
      year = {2015}
    }
    
  8. Paige, B., Wood, F., Doucet, A., & Teh, Y. W. (2014). Asynchronous Anytime Sequential Monte Carlo. ArXiv Preprint ArXiv:1407.2864. BIB PDF
    @article{Paige-2014-arXiv,
      title = {Asynchronous Anytime Sequential {M}onte {C}arlo},
      author = {Paige, Brooks and Wood, Frank and Doucet, Arnaud and Teh, Yee Whye},
      journal = {arXiv preprint arXiv:1407.2864},
      year = {2014}
    }
    
  9. Perov, Y., & Wood, F. (2014). Learning Probabilistic Programs. ArXiv Preprint ArXiv:1407.2646. BIB PDF
    @article{Perov-2014-arXiv,
      title = {Learning Probabilistic Programs},
      author = {Perov, Yura and Wood, Frank},
      journal = {arXiv preprint arXiv:1407.2646},
      year = {2014}
    }
    
  10. Paige, B., & Wood, F. (2014). A Compilation Target for Probabilistic Programming Languages. ArXiv Preprint ArXiv:1403.0504. BIB PDF
    @article{paige2014compilation,
      title = {A Compilation Target for Probabilistic Programming Languages},
      author = {Paige, Brooks and Wood, Frank},
      journal = {arXiv preprint arXiv:1403.0504},
      year = {2014}
    }
    

Book Chapters

  1. Wood, F., & Perotte, A. (2014). Handbook of Mixed Membership Models and Their Applications. In E. E. E. Airoldi D. Blei & S. Fienberg (Eds.). Chapman and Hall/CRC. BIB
    @inbook{Wood-HMMTH-2014,
      author = {Wood, F. and Perotte, A.},
      year = {2014},
      chapter = {Mixed Membership Classification for Documents with Hierarchically Structured Labels},
      editor = {E. Airoldi, D. Blei, E. Erosheva and Fienberg, S.},
      title = {Handbook of Mixed Membership Models and Their Applications},
      publisher = {Chapman and Hall/CRC}
    }
    

Invited Papers

  1. Wood, F. (2011). Discussion of "The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling". In Artificial Intelligence and Statistics. BIB PDF
    @inproceedings{Wood-AISTATS-2011-response,
      author = {Wood, F.},
      booktitle = {Artificial Intelligence and Statistics},
      title = {Discussion of "The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling"},
      year = {2011},
      pages = {}
    }
    

Workshop Publications

  1. Stefan Webb, R. Z., Adam Golinski, & Wood, F. (2017). Principled Inference Networks in Deep Generative Models. In NIPS Workshop on Bayesian Deep Learning. BIB
    @inproceedings{webbnips2017workshop,
      title = {Principled Inference Networks in Deep Generative Models},
      author = {Stefan Webb, Adam Golinski, Robert Zinkov and Wood, Frank},
      booktitle = {NIPS Workshop on Bayesian Deep Learning},
      year = {2017}
    }
    
  2. Siddharth, N., Paige, B., Desmaison, A., van de Meent, J.-W., Wood, F., Goodman, N. D., … Torr, P. H. S. (2016). Inducing Interpretable Representations with Variational Autoencoders. In NIPS Workshop on Interpretable ML for Complex Systems. BIB
    @inproceedings{siddharth2016interpretable,
      author = {Siddharth, N. and Paige, Brooks and Desmaison, Alban and van de Meent, Jan-Willem and Wood, Frank and Goodman, Noah D. and Kohli, Pushmeet and Torr, Philip H.S.},
      booktitle = {NIPS Workshop on Interpretable ML for Complex Systems},
      title = {Inducing Interpretable Representations with Variational Autoencoders},
      year = {2016}
    }
    
  3. Le, T. A., Baydin, A. G., & Wood, F. (2016). Nested Compiled Inference for Hierarchical Reinforcement Learning. In NIPS Workshop on Bayesian Deep Learning. BIB
    @inproceedings{le2016nested,
      author = {Le, Tuan Anh and Baydin, Atılım Güneş and Wood, Frank},
      booktitle = {NIPS Workshop on Bayesian Deep Learning},
      title = {Nested Compiled Inference for Hierarchical Reinforcement Learning},
      year = {2016}
    }
    
  4. Rainforth, T., Cornish, R., Yang, H., & Wood, F. (2016). On the Pitfalls of Nested Monte Carlo. NIPS Workshop on Advances in Approximate Bayesian Inference. BIB PDF
    @article{rainforth2016nestedmc,
      title = {On the {P}itfalls of {N}ested {M}onte {C}arlo},
      author = {Rainforth, Tom and Cornish, Rob and Yang, Hongseok and Wood, Frank},
      year = {2016},
      journal = {NIPS Workshop on Advances in Approximate Bayesian Inference}
    }
    
  5. Janz, D., Paige, B., Rainforth, T., van de Meent, J.-W., & Wood, F. (2016). Probabilistic Structure Discovery in Time Series Data. NIPS Workshop on Artificial Intelligence for Data Science. BIB PDF
    @article{janz2016probstruct,
      title = {Probabilistic {S}tructure {D}iscovery in {T}ime {S}eries {D}ata},
      author = {Janz, David and Paige, Brooks and Rainforth, Tom and van de Meent, Jan-Willem and Wood, Frank},
      year = {2016},
      journal = {NIPS Workshop on Artificial Intelligence for Data Science}
    }
    
  6. Warrington, A., & Wood, F. (2016). Algorithmic Optimisation of Neuron Generator Parameters. Frontiers in Neuroinformatics, (46). https://doi.org/10.3389/conf.fninf.2016.20.00046 ABS BIB

    @article{10.3389/conf.fninf.2016.20.00046,
      author = {Warrington, Andrew and Wood, Frank},
      title = {Algorithmic Optimisation of Neuron Generator Parameters},
      journal = {Frontiers in Neuroinformatics},
      volume = {},
      year = {2016},
      number = {46},
      url = {http://www.frontiersin.org/neuroinformatics/10.3389/conf.fninf.2016.20.00046/full},
      doi = {10.3389/conf.fninf.2016.20.00046},
      issn = {1662-5196}
    }
    
  7. van de Meent, J.-W., Paige, T. B., Tolpin, D., & Wood, F. (2016). An Interface for Black Box Learning in Probabilistic Programs. In POPL Workshop on Probabilistic Programming Semantics. BIB
    @inproceedings{vandemeent-PPS-2016,
      author = {van de Meent, Jan-Willem and Paige, T.~Brooks and Tolpin, David and Wood, Frank},
      booktitle = {POPL Workshop on Probabilistic Programming Semantics},
      title = {An Interface for Black Box Learning in Probabilistic Programs},
      year = {2016}
    }
    
  8. Staton, S., amd Chris Heunen, H. Y., Kammar, O., & Wood, F. (2016). Semantics of Higher-order Probabilistic Programs. In POPL Workshop on Probabilistic Programming Semantics. BIB
    @inproceedings{staton-PPS-2016,
      author = {Staton, Sam and amd Chris Heunen, Hongseok Yang and Kammar, Ohad and Wood, Frank},
      booktitle = {POPL Workshop on Probabilistic Programming Semantics},
      title = {Semantics of Higher-order Probabilistic Programs},
      year = {2016}
    }
    
  9. Paige, T. B., & and Wood, F. (2015). Inference Networks for Graphical Models. In NIPS Workshop on Advances in Approximate Bayesian Inference. BIB PDF
    @inproceedings{PaigeWood-INFNET-NIPS-2015,
      author = {Paige, T.~Brooks and and Wood, Frank},
      booktitle = {NIPS Workshop on Advances in Approximate Bayesian Inference},
      title = {Inference Networks for Graphical Models},
      year = {2015}
    }
    
  10. Rainforth, T., & Wood, F. (2015). Bayesian Optimization for Probabilistic Programs. In NIPS Workshop on Black Box Learning and Inference. BIB PDF
    @inproceedings{Rainforth-NIPS-PPWORKSHOP15,
      author = {Rainforth, Tom and Wood, Frank},
      booktitle = {NIPS Workshop on Black Box Learning and Inference},
      title = {Bayesian Optimization for Probabilistic Programs},
      year = {2015}
    }
    
  11. Perov, Y., Le, T.-A., & Wood, F. (2015). Data-driven Sequential Monte Carlo in Probabilistic Programming. In NIPS Workshop on Black Box Learning and Inference. BIB PDF
    @inproceedings{Perov_DD_SMC_in_PP,
      author = {Perov, Yura and Le, Tuan-Anh and Wood, Frank},
      booktitle = {NIPS Workshop on Black Box Learning and Inference},
      title = {Data-driven Sequential {M}onte {C}arlo in Probabilistic Programming},
      year = {2015}
    }
    
  12. Perov, Y. N., & Wood, F. (2014). Learning probabilistic programs. In NIPS Probabilistic Programming Workshop. BIB
    @inproceedings{Perov-2014-NIPSPROBPROG,
      author = {Perov, Y. N. and Wood, F.},
      booktitle = {NIPS Probabilistic Programming Workshop},
      title = {Learning probabilistic programs},
      year = {2014}
    }
    
  13. van de Meent, J. W., Yang, H., Mansinghka, V., & Wood, F. (2014). Particle Gibbs with Ancestor Resampling for Probabilistic Programs. In NIPS Probabilistic Programming Workshop. BIB
    @inproceedings{vandeMeent-2014-NIPSPROBPROG,
      author = {van~de~Meent, J. W. and Yang, H. and Mansinghka, V. and Wood, F.},
      booktitle = {NIPS Probabilistic Programming Workshop},
      title = {Particle {G}ibbs with Ancestor Resampling for Probabilistic Programs},
      year = {2014}
    }
    
  14. Tolpin, D., van de Meent, J. W., Paige, B., & Wood, F. (2014). Adaptive Scheduling in MCMC and Probabilistic Programming. In NIPS Probabilistic Programming Workshop. BIB
    @inproceedings{Tolpin-2014-NIPSPROBPROG,
      author = {Tolpin, D. and van~de~Meent, J. W. and Paige, B. and Wood, F.},
      booktitle = {NIPS Probabilistic Programming Workshop},
      title = {Adaptive Scheduling in {MCMC} and Probabilistic Programming},
      year = {2014}
    }
    
  15. Paige, T. B., Zhang, X., Forde, J., & Wood, F. (2012). Perspective Inference for Eye-to-Eye Videoconferencing: Empirical Evaluation Tools and Data. In Proc. 7th Annual Machine Learning Workshop, New York Academy of Sciences. BIB
    @inproceedings{Paige-NYMLW-2012,
      title = {Perspective Inference for Eye-to-Eye Videoconferencing: Empirical Evaluation Tools and Data},
      author = {Paige, T. B. and Zhang, X. and Forde, J. and Wood, F.},
      booktitle = {Proc. 7th Annual Machine Learning Workshop, New York Academy of Sciences},
      year = {2012}
    }
    
  16. Wood, F. (2011). Modeling Streaming Data In the Absence of Sufficiency. In NIPS Nonparametric Bayes Workshop. BIB PDF
    @inproceedings{Wood-NIPSNPBAYES-2011,
      author = {Wood, F.},
      booktitle = {NIPS Nonparametric {B}ayes Workshop},
      title = {Modeling Streaming Data In the Absence of Sufficiency},
      year = {2011}
    }
    
  17. Wood, F., & Teh, Y. W. (2008). A Hierarchical, Hierarchical Pitman Yor Process Language Model. In ICML/UAI Nonparametric Bayes Workshop. BIB PDF
    @inproceedings{Wood-ICMLNPBAYES-2008,
      author = {Wood, F. and Teh, Y.W.},
      booktitle = {ICML/UAI Nonparametric {B}ayes Workshop},
      title = {A Hierarchical, Hierarchical {P}itman {Y}or Process Language Model},
      year = {2008}
    }
    
  18. Grollman, D. H., Jenkins, O. C., & Wood, F. (2005). Discovering Natural Kinds of Robot Sensory Experiences in Unstructured Environments. In Advances in Neural Information Processing Systems Workshop on Machine Learning Based Robotics in Unstructured Environments. BIB
    @inproceedings{Grollman-NIPS-2005,
      author = {Grollman, D. H. and Jenkins, O. C. and Wood, F.},
      booktitle = {Advances in Neural Information Processing Systems Workshop on Machine Learning Based Robotics in Unstructured Environments},
      title = {Discovering Natural Kinds of Robot Sensory Experiences in Unstructured Environments},
      year = {2005}
    }