Stochastic expectation propagation

Advances in Neural Information Processing Systems

Volumn 2015-January, Issue , 2015, Pages 2323-2331

  Li, Yingzhen ^a   Hernández Lobato, José Miguel ^b   Turner, Richard E ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b HARVARD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; INFORMATION SCIENCE; ITERATIVE METHODS; STOCHASTIC SYSTEMS;

APPROXIMATE BAYESIAN; COMPUTATIONAL ADVANTAGES; DETERMINISTIC APPROXIMATION; EXPECTATION PROPAGATION; LARGE-SCALE DATASET; POSTERIOR DISTRIBUTIONS; REAL-WORLD DATASETS; VARIATIONAL INFERENCE;

APPROXIMATION ALGORITHMS;

EID: 84965104740     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. Sungjin Ahn, Babak Shahbaba, and Max Welling. Distributed stochastic gradient mcmc. In Proceedings of the 31st International Conference on Machine Learning (ICML-14), pages 1044-1052, 2014.
2. Rémi Bardenet, Arnaud Doucet, and Chris Holmes. Towards scaling up markov chain monte carlo: an adaptive subsampling approach. In Proceedings of the 31st International Conference on Machine Learning (ICML-14), pages 405-413, 2014.
3. Matthew D. Hoffman, David M. Blei, Chong Wang, and John William Paisley. Stochastic variational inference. Journal of Machine Learning Research, 14(1):1303-1347, 2013.
4. José Miguel Hernández-Lobato and Ryan P. Adams. Probabilistic backpropagation for scalable learning of bayesian neural networks. arXiv:1502.05336, 2015.
5. Andrew Gelman, Aki Vehtari, Pasi Jylnki, Christian Robert, Nicolas Chopin, and John P. Cunningham. Expectation propagation as a way of life. arXiv:1412.4869, 2014.
6. Minjie Xu, Balaji Lakshminarayanan, Yee Whye Teh, Jun. Zhu, and Bo Zhang. Distributed bayesian posterior sampling via moment sharing. In NIPS, 2014.
7. Thomas P. Minka. Expectation propagation for approximate Bayesian inference. In Uncertainty in Artificial Intelligence, volume 17, pages 362-369, 2001.
8. Manfred Opper and Ole Winther. Expectation consistent approximate inference. The Journal of Machine Learning Research, 6:2177-2204, 2005.
9. Malte Kuss and Carl Edward Rasmussen. Assessing approximate inference for binary gaussian process classification. The Journal of Machine Learning Research, 6:1679-1704, 2005.
10. Simon Barthelmé and Nicolas Chopin. Expectation propagation for likelihood-free inference. Journal of the American Statistical Association, 109(505):315-333, 2014.
11. John P Cunningham, Philipp Hennig, and Simon Lacoste-Julien. Gaussian probabilities and expectation propagation. arXiv preprint arXiv:1111.6832, 2011.
12. ∗∗2004.
13. John M Winn and Christopher M Bishop. Variational message passing. In Journal of Machine Learning Research, pages 661-694, 2005.
14. Michael I Jordan, Zoubin Ghahramani, Tommi S Jaakkola, and Lawrence K Saul. An introduction to variational methods for graphical models. Machine learning, 37(2):183-233, 1999.
15. Matthew James Beal. Variational algorithms for approximate Bayesian inference. PhD thesis, University of London, 2003.
16. Richard E. Turner and Maneesh Sahani. Two problems with variational expectation maximisation for time-series models. In D. Barber, T. Cemgil, and S. Chiappa, editors, Bayesian Time series models, chapter 5, pages 109-130. Cambridge University Press, 2011.
17. Richard E. Turner and Maneesh Sahani. Probabilistic amplitude and frequency demodulation. In J. Shawe-Taylor, R. S. Zemel, P. Bartlett, F. C. N. Pereira, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 24, pages 981-989. 2011.
18. Ralf Herbrich, Tom Minka, and Thore Graepel. Trueskill: A bayesian skill rating system. In Advances in Neural Information Processing Systems, pages 569-576, 2006.
19. Peter S. Maybeck. Stochastic models, estimation and control. Academic Press, 1982.
20. Yuan Qi, Ahmed H Abdel-Gawad, and Thomas P Minka. Sparse-posterior gaussian processes for general likelihoods. In Uncertainty and Artificial Intelligence (UAI), 2010.
21. Shun-ichi Amari and Hiroshi Nagaoka. Methods of information geometry, volume 191. Oxford University Press, 2000.
22. Herbert Robbins and Sutton Monro. A stochastic approximation method. The annals of mathematical statistics, pages 400-407, 1951.
23. Guillaume Dehaene and Simon Barthelmé. Expectation propagation in the large-data limit. arXiv:1503.08060, 2015.
24. Thomas Minka. Divergence measures and message passing. Technical Report MSR-TR-2005-173, Microsoft Research, Cambridge, 2005.
25. Matthew D Hoffman and Andrew Gelman. The no-u-turn sampler: Adaptively setting path lengths in hamiltonian monte carlo. The Journal of Machine Learning Research, 15(1):1593-1623, 2014.