On the use of variational inference for learning discrete graphical models

Proceedings of the 28th International Conference on Machine Learning, ICML 2011

Volumn , Issue , 2011, Pages 1009-1016

  Yang, Eunho ^a   Ravikumar, Pradeep ^a  

^a University of Texas at Austin   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATE INFERENCE; APPROXIMATE LOG-LIKELIHOOD; CLOSED FORM; GENERAL CLASS; GRAPH STRUCTURES; GRAPHICAL MODEL; MESSAGE PASSING ALGORITHM; PARTITION FUNCTIONS; STRUCTURE-BASED; VARIATIONAL APPROXIMATION; VARIATIONAL INFERENCE;

ESTIMATION; GRAPHIC METHODS; LEARNING SYSTEMS; SPEECH RECOGNITION;

MESSAGE PASSING;

EID: 80053446668     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. Boyd, S. and vandenberghe, L. Convex optimization. Cambridge University Press, Cambridge, UK, 2004.
2. Candes, E. and Tao, T. The Dantzig selector: Statistical estimation when p is much larger than n. Annals of Statistics, 35(6):2313-2351, 2007.
3. Censor, Y. and Zenios, S. A. Parallel Optimization: Theory, Algorithms, and Applications. Numerical Mathematics and Scientific Computation. Oxford University Press, 1988.
4. Heskes, T. Convexity arguments for efficient minimization of the bethe and kikuchi free energies. J. Artif. Intell. Res. (JAIR), 26:153-190, 2006. (Pubitemid 44452210)
5. Ising, E. Beitrag zur theorie der ferromagnetismus. Zeitschrift für Physik, 31:253-258, 1925.
6. Lee, S.-I., Ganapathi, V., and Koller, D. Efficient structure learning of markov networks using l1-regularization. In NIPS 19, 2007.
7. Montanari, A. and Pereira, J. A. Which graphical models are difficult to learn. In Neural Information Processing Systems (NIPS), 2009.
8. Nesterov, Y. Introductory lectures on convex optimization: a basic course. 2004.
9. Ortega, J. M. and Rheinboldt, W. C. Iterative solution of nonlinear equations in several variables. Classics in applied mathematics. SIAM, New York, 2000.
10. Pearl, J. Probabilistic reasoning in intelligent systems. Morgan Kaufman, San Mateo, 1988.
11. 1-penalized log-determinant divergence. Technical Report 767, Dept. of Stat., UC Berkeley, 2008.
12. 1-regularized logistic regression. Ann. Stat., 38(3):1287-1319, 2010.
13. Tropp, J. A. Just relax: Convex programming methods for identifying sparse signals. IEEE Trans. Info. Theory, 51(3):1030-1051, March 2006. (Pubitemid 46444890)
14. Wainwright, M. J. Estimating the "wrong" graphical model: Benefits in the computation-limited regime. JMLR, 7:1829-1859, September 2006. (Pubitemid 44395190)
15. 1-constrained quadratic programming (lasso). IEEE Transactions on Information Theory, 55:2183-2202, 2009.
16. Wainwright, M. J. and Jordan, M. I. Graphical models, exponential families, and variational inference. Foundations and Trends in Machine Learning, 1(1-2):1-305, 2008.
17. Wainwright, M. J., Jaakkola, T. S., and Willsky, A. S. Tree-reweighted belief propagation algorithms and approximate ML estimation by pseudomoment matching. In AISTATS, 2003.
18. Wainwright, M. J., Jaakkola, T. S., and Willsky, A. S. A new class of upper bounds on the log partition function. IEEE Trans. Info. Theory, 51(7):2313-2335, July 2005. (Pubitemid 41136395)
19. Weiss, Y., Yanover, C., and Meltzer, T. MAP estimation, linear programming, and belief propagation with convex free energies. In UAI, 2007.
20. Yedidia, J. S., Freeman, W. T., and Weiss, Y. Generalized belief propagation. In NIPS 13, pp. 689-695, 2001.