Gaussian covariance and scalable variational inference

ICML 2010 - Proceedings, 27th International Conference on Machine Learning

Volumn , Issue , 2010, Pages 967-974

  Seeger, Matthias W ^a  

^a SAARLAND UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATE INFERENCE; BAYESIAN EXPERIMENTAL DESIGNS; COMPUTATIONAL ASPECTS; COVARIANCE APPROXIMATIONS; DECISION OUTCOME; GAUSSIANS; LARGE-SCALE APPLICATIONS; LINEAR MODEL; LOW RANK APPROXIMATIONS; STATISTICAL CONSEQUENCES; VARIATIONAL INFERENCE;

LEARNING SYSTEMS;

VARIATIONAL TECHNIQUES;

EID: 77956540504     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. Attias, H. A variational Bayesian framework for graphical models. In NIPS 12, pp. 209-215, 2000.
2. Banerjee, O., El Ghaoui, L., and d'Aspremont, A. Model selection through sparse maximum likelihood estimation for multivariate Gaussian or binary data. JMLR, 9:485-516, 2008.
3. Davis, C. All convex invariant functions of Hermitian matrices. Archiv der Mathematik, 8:276-278, 1957.
4. Golub, G. and Van Loan, C. Matrix Computations. Johns Hopkins University Press, 3rd edition, 1996.
5. Levin, A., Weiss, Y., Durand, F., and Freeman, W. Understanding and evaluating blind deconvolution algorithms. In CVPR, 2009.
6. Malioutov, D., Johnson, J., Choi, M., and Willsky, A. Low-rank variance estimation in GMRF models: Single and multiscale approaches. IEEE Trans. Sig. Proc, 56(10):4621-4634, 2008.
7. Nickisch, H. and Seeger, M. Convex variational Bayesian inference for large scale generalized linear models. In ICML 26, pp. 761-768, 2009.
8. Schneider, M. and Willsky, A. Krylov subspace estimation. SIAM J. Comp., 22(5):1840-1864, 2001.
9. Seeger, M. Bayesian inference and optimal design for the sparse linear model. JMLR, 9:759-813, 2008.
10. Seeger, M. Speeding up magnetic resonance image acquisition by Bayesian multi-slice adaptive compressed sensing. In NIPS 22, pp. 1633-1641, 2009.
11. Seeger, M. and Nickisch, H. Compressed sensing and Bayesian experimental design. In ICML 25, 2008.
12. Seeger, M., Nickisch, H., Pohmann, R., and Schölkopf, B. Bayesian experimental design of magnetic resonance imaging sequences. In NIPS 21, pp. 1441-1448, 2009.
13. Willsky, A. Multiresolution Markov models for signal and image processing. Proc. IEEE, 8(90):1396-1458, 2002.
14. Wipf, D. and Nagarajan, S. A new view of automatic relevance determination. In NIPS 20, 2008.