Approximate inference for the loss-calibrated Bayesian

Journal of Machine Learning Research

Volumn 15, Issue , 2011, Pages 416-424

  Lacoste Julien, Simon ^a   Huszár, Ferenc ^a   Ghahramani, Zoubin ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATE INFERENCE; BAYESIAN DECISION THEORY; DECISION TASK; GAUSSIAN PROCESS CLASSIFICATIONS; IMPROVE-A; RESEARCH DIRECTIONS;

ARTIFICIAL INTELLIGENCE; DECISION THEORY;

INFERENCE ENGINES;

EID: 84862277158     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (22)

1. G. H. Bakir, T. Hofmann, B. Schölkopf, A. J. Smola, B. Taskar, and S. V. N. Vishwanathan. Predicting Structured Data. The MIT Press, 2007.
2. P. L. Bartlett, M. I. Jordan, and J. D. McAuliffe. Convexity, classification, and risk bounds. Journal of the American Statistical Association, 101(473):138-156, 2006.
3. J. O. Berger. Statistical Decision Theory and Bayesian Analysis. Springer, New York, 1985.
4. L. Csató. Gaussian Processes - Iterative Sparse Approximations. PhD thesis, Aston University, 2002.
5. A. P. Dawid. Proper measures of discrepancy, uncertainty and dependence with applications to predictive experimental designs. Technical Report 139, Department of Statistical Science at University College London, 1994. (revised in 1998).
6. A. Dempster, N. Laird, and D. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 39(1):1-38, 1977.
7. Z. Ghahramani and M. I. Jordan. Factorial hidden Markov models. Machine Learning, 29:245-275, 1997.
8. T. Jaakkola, M. Meila, and T. Jebara. Maximum entropy discrimination. In Advances in Neural Information Processing Systems 12. MIT Press, Cambridge, MA, 1999.
9. T. Jebara. Multitask sparsity via Maximum Entropy Discrimination. Journal of Machine Learning Research, 12:75-110, 2011.
10. T. Joachims, T. Finley, and C.-N. Yu. Cutting-plane training of structural SVMs. Machine Learning, 77 (1):27-59, 2009.
11. M. I. Jordan, Z. Ghahramani, T. S. Jaakkola, and L. K. Saul. An introduction to variational methods for graphical models. In M. I. Jordan, editor, Learning in Graphical Models. MIT Press, Cambridge, 1999.
12. T. P. Minka. A family of algorithms for approximate Bayesian inference. PhD thesis, Massachusetts Institute of Technology, 2001.
13. R. M. Neal. MCMC using Hamiltonian dynamics. In G. J. S. Brooks, A. Gelman and X.-L. Meng, editors, Handbook of Markov Chain Monte Carlo. Chapman & Hall / CRC Press, 2010.
14. H. Nickisch and C. E. Rasmussen. Approximations for binary Gaussian process classification. Journal of Machine Learning Research, 9:2035-2078, 2008.
15. J. Quińonero-Candela and C. E. Rasmussen. A unifying view of sparse approximate Gaussian process regression. Journal of Machine Learning Research, 6:1935-1959, 2005.
16. C. E. Rasmussen and C. K. I. Williams. Gaussian Processes for Machine Learning. The MIT Press, Cambridge, MA, USA, 2006.
17. C. P. Robert. The Bayesian Choice. Springer, New York, 2001.
18. M. J. Schervish. Theory of Statistics. Springer, New York, 1995.
19. E. Snelson. Flexible and efficient Gaussian process models for machine learning. PhD thesis, Gatsby Computational Neuroscience Unit, University College London, 2007.
20. I. Steinwart and A. Christmann. Support Vector Machines. Springer, New York, 2008.
21. V. Stoyanov, J. Eisner, and A. Ropson. Empirical risk minimization of graphical model parameters given approximate inference, decoding, and model structure. In G. Gordon and D. Dunson, editors, Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, volume 15, Fort Lauderdale, FL, USA, April 2011. Journal of Machine Learning Research.
22. M. Sugiyama, M. Krauledat, and K.-R. Müller. Covariate shift adaptation by importance weighted cross validation. Journal of Machine Learning Research, 8:985-1005, 2007.