Large scale nonparametric Bayesian inference: Data parallelisation in the Indian Buffet process

Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference

Volumn , Issue , 2009, Pages 1294-1302

  Doshi Velez, Finale ^a   Knowles, David ^a   Mohamed, Shakir ^a   Ghahramani, Zoubin ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; INFERENCE ENGINES;

BAYESIAN METHODS; COMPLEX DATASETS; DATA PARALLELIZATION; HIGH-DIMENSIONAL; HIGHER-DIMENSIONAL; LARGE DATASETS; LARGE-SCALES; NON-PARAMETRIC BAYESIAN INFERENCES; NON-PARAMETRIC BAYESIAN MODELING; PROBABILISTIC MODELS;

MESSAGE PASSING;

EID: 84858731987     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. C. Chu, S. Kim, Y. Lin, Y. Yu, G. Bradski, A. Ng, and K. Olukotun, "Map-reduce for machine learning on multicore," in Advances in Neural Information Processing Systems, p. 281, MIT Press, 2007.
2. A. Asuncion, P. Smyth, and M. Welling, "Asynchronous distributed learning of topic models," in Advances in Neural Information Processing Systems 21, 2008.
3. T. Griffiths and Z. Ghahramani, "Infinite latent feature models and the Indian buffet process," in Advances in Neural Information Processing Systems, vol. 16, NIPS, 2006.
4. F. Doshi-Velez and Z. Ghahramani, "Accelerated inference for the Indian buffet process," in International Conference on Machine Learning, 2009.
5. E. Meeds, Z. Ghahramani, R. Neal, and S. Roweis, "Modeling dyadic data with binary latent factors," in Advances in Neural Information Processing Systems, vol. 19, pp. 977-984, 2007.
6. Y. W. Teh, D. Görür, and Z. Ghahramani, "Stick-breaking construction for the Indian buffet process," in Proceedings of the Intl. Conf. on Artificial Intelligence and Statistics, vol. 11, pp. 556-563, 2007.
7. F. Wood and T. L. Griffiths, "Particle filtering for nonparametric Bayesian matrix factorization," in Advances in Neural Information Processing Systems, vol. 19, pp. 1513-1520, 2007.
8. F. Doshi-Velez, K. T. Miller, J. Van Gael, and Y. W. Teh, "Variational inference for the Indian buffet process," in Proceedings of the Intl. Conf. on Artificial Intelligence and Statistics, vol. 12, pp. 137-144, 2009.
9. S. P. Brooks and G. O. Roberts, "Convergence assessment techniques for Markov Chain Monte Carlo," Statistics and Computing, vol. 8, pp. 319-335, 1998.
10. C. R. Robert and G. Casella, Monte Carlo Statistical Methods. Springer, second ed., 2004.
11. A. M. Mart'inez and A. C. Kak, "PCA versus LDA," IEEE Trans. Pattern Anal. Mach. Intelligence, vol. 23, pp. 228-233, 2001.
12. G. E. Poliner and D. P. W. Ellis, "A discriminative model for polyphonic piano transcription," EURASIP J. Appl. Signal Process., vol. 2007, no. 1, pp. 154-154, 2007.
13. T. Kollar and N. Roy, "Utilizing object-object and object-scene context when planning to find things.," in International Conference on Robotics and Automation, 2009.
14. C. G. Joseph Gonzalez, Yucheng Low, "Residual splash for optimally parallelizing belief propagation," in Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (D. van Dyk and M. Welling, eds.), vol. 5, pp. 177-184, JMLR, 2009.
15. D. Stern, R. Herbrich, and T. Graepel, "Matchbox: Large scale online Bayesian recommendations," in 18th International World Wide Web Conference (WWW2009), April 2009.
16. R. Nallapati, W. Cohen, and J. Lafferty, "Parallelized variational EM for Latent Dirichlet Allocation: An experimental evaluation of speed and scalability," in ICDMW '07: Proceedings of the Seventh IEEE International Conference on Data Mining Workshops, (Washington, DC, USA), pp. 349-354, IEEE Computer Society, 2007.
17. D. Newman, A. Asuncion, P. Smyth, and M. Welling, "Distributed inference for Latent Dirich-let Allocation," in Advances in Neural Information Processing Systems 20 (J. Platt, D. Koller, Y. Singer, and S. Roweis, eds.), pp. 1081-1088, Cambridge, MA: MIT Press, 2008.