Streaming, distributed variational inference for Bayesian nonparametrics

Advances in Neural Information Processing Systems

Volumn 2015-January, Issue , 2015, Pages 280-288

  Campbell, Trevor ^a   Straub, Julian ^b   Fisher, John W ^b   How, Jonathan P ^a  

^a LIDS ^*  (United States)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMBINATORIAL OPTIMIZATION; INFORMATION SCIENCE; OPTIMIZATION;

BAYESIAN NONPARAMETRICS; COMBINATORIAL OPTIMIZATION PROBLEMS; DISTRIBUTED INFERENCE; LEARNING RATES; PROCESSING NODES; SCALABILITY AND PERFORMANCE; SOLUTION TECHNIQUES; VARIATIONAL INFERENCE;

INFERENCE ENGINES;

EID: 84965170281     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (26)

1. Agostino Nobile. Bayesian Analysis of Finite Mixture Distributions. PhD thesis, Carnegie Mellon University, 1994.
2. Jeffrey W. Miller and Matthew T. Harrison. A simple example of Dirichlet process mixture inconsistency for the number of components. In Advances in Neural Information Processing Systems 26, 2013.
3. Yee Whye Teh. Dirichlet processes. In Encyclopedia of Machine Learning. Springer, New York, 2010.
4. Thomas L. Griffiths and Zoubin Ghahramani. Infinite latent feature models and the Indian buffet process. In Advances in Neural Information Processing Systems 22, 2005.
5. Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia C. Wilson, and Michael I. Jordan. Streaming variational Bayes. In Advances in Neural Information Procesing Systems 26, 2013.
6. Trevor Campbell and Jonathan P. How. Approximate decentralized Bayesian inference. In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, 2014.
7. Dahua Lin. Online learning of nonparametric mixture models via sequential variational approximation. In Advances in Neural Information Processing Systems 26, 2013.
8. Xiaole Zhang, David J. Nott, Christopher Yau, and Ajay Jasra. A sequential algorithm for fast fitting of Dirichlet process mixture models. Journal of Computational and Graphical Statistics, 23(4):1143-1162, 2014.
9. Matt Hoffman, David Blei, Chong Wang, and John Paisley. Stochastic variational inference. Journal of Machine Learning Research, 14:1303-1347, 2013.
10. Chong Wang, John Paisley, and David M. Blei. Online variational inference for the hierarchical Dirichlet process. In Proceedings of the 11th International Conference on Artificial Intelligence and Statistics, 2011.
11. Michael Bryant and Erik Sudderth. Truly nonparametric online variational inference for hierarchical Dirichlet processes. In Advances in Neural Information Proecssing Systems 23, 2009.
12. Chong Wang and David Blei. Truncation-free stochastic variational inference for Bayesian nonparametric models. In Advances in Neural Information Processing Systems 25, 2012.
13. Michael Hughes and Erik Sudderth. Memoized online variational inference for Dirichlet process mixture models. In Advances in Neural Information Processing Systems 26, 2013.
14. Jason Chang and John Fisher III. Parallel sampling of DP mixture models using sub-clusters splits. In Advances in Neural Information Procesing Systems 26, 2013.
15. Willie Neiswanger, Chong Wang, and Eric P. Xing. Asymptotically exact, embarassingly parallel MCMC. In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, 2014.
16. Carlos M. Carvalho, Hedibert F. Lopes, Nicholas G. Polson, and Matt A. Taddy. Particle learning for general mixtures. Bayesian Analysis, 5(4):709-740, 2010.
17. Matthew Stephens. Dealing with label switching in mixture models. Journal of the Royal Statistical Society: Series B, 62(4):795-809, 2000.
18. Ajay Jasra, Chris Holmes, and David Stephens. Markov chain Monte Carlo methods and the label switching problem in Bayesian mixture modeling. Statistical Science, 20(1):50-67, 2005.
19. Yee Whye Teh, Michael I. Jordan, Matthew J. Beal, and David M. Blei. Hierarchical Dirichlet processes. Journal of the American Statistical Association, 101(476):1566-1581, 2006.
20. Finale Doshi-Velez and Zoubin Ghahramani. Accelerated sampling for the Indian buffet process. In Proceedings of the International Conference on Machine Learning, 2009.
21. Avinava Dubey, Sinead Williamson, and Eric Xing. Parallel Markov chain Monte Carlo for Pitman-Yor mixture models. In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, 2014.
22. Jack Edmonds and Richard Karp. Theoretical improvements in algorithmic efficiency for network flow problems. Journal of the Association for Computing Machinery, 19:248-264, 1972.
23. Jim Pitman. Exchangeable and partially exchangeable random partitions. Probability Theory and Related Fields, 102(2):145-158, 1995.
24. David M. Blei and Michael I. Jordan. Variational inference for Dirichlet process mixtures. Bayesian Analysis, 1(1):121-144, 2006.
25. Yann Le Cun, Corinna Cortes, and Christopher J. C. Burges. MNIST database of handwritten digits. Online: yann. lecun. com/exdb/mnist.
26. Jianxiong Xiao, James Hays, Krista A. Ehinger, Aude Oliva, and Antonio Torralba. SUN 397 image database. Online: vision. cs.princeton. edu/projects/2010/SUN.