Bayesian Nonparametrics

Bayesian Nonparametrics

  • Sharing features among dynamical systems with beta processes. E. Fox, E. Sudderth, M. I. Jordan, and A. S. Willsky. In Y. Bengio, D. Schuurmans, J. Lafferty and C. Williams (Eds.) Advances in Neural Information Processing Systems (NIPS) 22, 2010.

  • Nonparametric latent feature models for link prediction. K. Miller, T. Griffiths, and M. I. Jordan. In Y. Bengio, D. Schuurmans, J. Lafferty and C. Williams (Eds.) Advances in Neural Information Processing Systems (NIPS) 22, 2010.

  • Hierarchical Bayesian nonparametric models with applications. Y. W. Teh and M. I. Jordan. In N. Hjort, C. Holmes, P. Mueller, and S. Walker (Eds.), Bayesian Nonparametrics: Principles and Practice, Cambridge, UK: Cambridge University Press, to appear.

  • The nested Chinese restaurant process and Bayesian inference of topic hierarchies. D. M. Blei, T. Griffiths, and M. I. Jordan. Journal of the ACM, to appear. [Software].

  • Shared segmentation of natural scenes using dependent Pitman-Yor processes. E. Sudderth and M. I. Jordan. In D. Koller, Y. Bengio, D. Schuurmans and L. Bottou (Eds.), Advances in Neural Information Processing Systems (NIPS) 21, to appear.

  • The sticky HDP-HMM: Bayesian nonparametric hidden Markov models with persistent states. E. Fox, E. Sudderth, M. I. Jordan, and A. Willsky. Technical Report P-2777, MIT LIDS, 2009.

  • Nonparametric Bayesian identification of jump systems with sparse dependencies. E. Fox, E. Sudderth, M. I. Jordan, and A. Willsky. 15th IFAC Symposium on System Identification (SYSID), St. Malo, France, 2009.

  • Probabilistic grammars and hierarchical Dirichlet processes. P. Liang, M. I. Jordan, and D. Klein. In T. O'Hagan and M. West (Eds.), The Handbook of Applied Bayesian Analysis, Oxford University Press, to appear.

  • Posterior consistency of the Silverman g-prior in Bayesian model choice. Z. Zhang and M. I. Jordan. In D. Koller, Y. Bengio, D. Schuurmans and L. Bottou (Eds.), Advances in Neural Information Processing Systems (NIPS) 21, to appear.

  • Nonparametric Bayesian learning of switching linear dynamical systems. E. B. Fox, E. Sudderth, M. I. Jordan, and A. S. Willsky. In D. Koller, Y. Bengio, D. Schuurmans and L. Bottou (Eds.), Advances in Neural Information Processing Systems (NIPS) 21, to appear. [Long version].

  • The phylogenetic Indian buffet process: A non-exchangeable nonparametric prior for latent features. K. Miller, T. Griffiths and M. I. Jordan. In Uncertainty in Artificial Intelligence (UAI), Proceedings of the Twenty-Fourth Conference, 2008.

  • An HDP-HMM for systems with state persistence. E. Fox, E. Sudderth, M. I. Jordan, and A. Willsky. Proceedings of the 25th International Conference on Machine Learning (ICML), 2008. [Long version].

  • The infinite PCFG using hierarchical Dirichlet processes. P. Liang, S. Petrov, M. I. Jordan, and D. Klein. Empirical Methods in Natural Language Processing (EMNLP), 2007.

  • A permutation-augmented sampler for DP mixture models. P. Liang, M. I. Jordan, and B. Taskar. Proceedings of the 24th International Conference on Machine Learning (ICML), 2007.

  • Hierarchical beta processes and the Indian buffet process. R. Thibaux, and M. I. Jordan. Proceedings of the Conference on Artificial Intelligence and Statistics (AISTATS), 2007.

  • Learning multiscale representations of natural scenes using Dirichlet processes. J. J. Kivinen, E. B. Sudderth, and M. I. Jordan. IEEE International Conference on Computer Vision (ICCV), 2007.

  • Bayesian haplotype inference via the Dirichlet process. E. P. Xing, M. I. Jordan and R. Sharan. Journal of Computational Biology, 14, 267-284, 2007.

  • Image denoising with nonparametric hidden Markov trees. J. J. Kivinen, E. B. Sudderth, and M. I. Jordan. IEEE International Conference on Image Processing (ICIP), 2007.

  • Hierarchical Dirichlet processes. Y. W. Teh, M. I. Jordan, M. J. Beal and D. M. Blei. Journal of the American Statistical Association, 101, 1566-1581, 2006. [Software]

  • Bayesian multi-population haplotype inference via a hierarchical Dirichlet process mixture. E. P. Xing, K.-A. Song, M. I. Jordan, and Y. W. Teh. Proceedings of the 23rd International Conference on Machine Learning (ICML), 2006.

  • Bayesian multicategory support vector machines. Z. Zhang, and M. I. Jordan. In Uncertainty in Artificial Intelligence (UAI), Proceedings of the Twenty-Second Conference, 2006.

  • Nonparametric empirical Bayes for the Dirichlet process mixture model. J. D. McAuliffe, D. M. Blei and M. I. Jordan. Statistics and Computing, 16, 5-14, 2006.

  • Variational inference for Dirichlet process mixtures. D. M. Blei and M. I. Jordan. Bayesian Analysis, 1, 121-144, 2005.

  • Dirichlet processes, Chinese restaurant processes and all that. M. I. Jordan. Tutorial presentation at the NIPS Conference, 2005.

  • Gaussian processes and the null-category noise model. N. D. Lawrence and M. I. Jordan. In O. Chapelle, B. Schoelkopf & A. Zien (Eds), Semi-Supervised Learning, Cambridge, MA: MIT Press, 2005.

  • Semi-supervised learning via Gaussian processes. N. D. Lawrence and M. I. Jordan. In L. Saul, Y. Weiss, and L. Bottou (Eds.), Advances in Neural Information Processing Systems (NIPS) 17, 2005.

  • Sharing clusters among related groups: Hierarchical Dirichlet processes. Y. W. Teh, M. I. Jordan, M. J. Beal and D. M. Blei. In L. Saul, Y. Weiss, and L. Bottou (Eds.), Advances in Neural Information Processing Systems (NIPS) 17, 2005. [Long version]. [Software]

  • Semiparametric latent factor models. Y. W. Teh, M. Seeger, and M. I. Jordan. In press, Proceedings of the Conference on Artificial Intelligence and Statistics (AISTATS), 2004.

  • Hierarchical topic models and the nested Chinese restaurant process. D. M. Blei, T. Griffiths, M. I. Jordan, and J. Tenenbaum. In S. Thrun, L. Saul, and B. Schoelkopf (Eds.), Advances in Neural Information Processing Systems (NIPS) 16, 2004.

  • Variational methods for the Dirichlet process. D. M. Blei and M. I. Jordan. Proceedings of the 21st International Conference on Machine Learning (ICML), 2004. [Long version].

  • Bayesian haplotype inference via the Dirichlet process. E. P. Xing, R. Sharan, and M. I. Jordan. Proceedings of the 21st International Conference on Machine Learning (ICML), 2004.