Learning Sparse Markov Networks
 

Sparse Gaussian MRFs

  1. H. Rue, L. Held. Gaussian Markov Random Fields: Theory and Applications, CRC Press, 2005.
  2. Havard Rue ,  Turid Follestad, Gaussian Markov Random Field Models. Tech Report, 2003.
  3. Jerome Friedman, Trevor Hastie and Robert Tibshirani (2007). Sparse inverse covariance estimation with the graphical lasso, Biostatistics, December 12,2007.
  4. Su-In Lee, Varun Ganapathi, Daphne Koller. Efficient Structure Learning of Markov Networks using L1-Regularization [bibtex]
  5. Nicolai Meinshausen and Peter Buhlmann (2006). High dimensional graphs and variable selection with the Lasso, Annals of Statistics 34(3), 1436-1462
    (arxiv:math/0608017, an interview with Essential Science Indicators in January 2008).
  6. Yuan, M. and Lin, Y. (2007), Model Selection and Estimation in the Gaussian Graphical Model, Biometrika, 94(1), 19-35.
  7. O. Banerjee, L. El Ghaoui, A. d’Aspremont. Model Selection Through Sparse Maximum Likelihood Estimation. ArXiv: 0707.0704, (local pdf file, COVSEL source code). JMLR 2008.
  8. J. Duchi, S. Gould, D. Koller. Projected Subgradient Methods for Learning Sparse Gaussians.  UAI 2008.
  9. J. Dahl, L. Vandenberghe, V. Roychowdury. Covariance selection for non-chordal graphs via chordal embedding. Optimization Methods and Software, 2008.
  10. Elizaveta Levina, Adam Rothman, Ji Zhu. Sparse estimation of large covariance matrices via a nested Lasso penalty. Annals of Applied Statistics, 2008.
  11. Adam J. Rothman, Peter J. Bickel, Elizaveta Levina, and Ji Zhu, Sparse Permutation Invariant Covariance Estimation, Electron. J. Statist. Volume 2, 494-515, 2008.
  12. Diego Vidaurre, Concha Bielza, Pedro Larrañaga. Learning an L1-Regularized Gaussian Bayesian Network in the Equivalence Class Space. IEEE Trans Syst Man Cybern B Cybern.  Jan 2010.
  13. H. Höfling, R. Tibshirani. Estimation of Sparse Binary Pairwise Markov Networks using Pseudo-likelihoods. Journal of Machine Learning Research, 2009.
  14. Vijay Krishnamurphy, Alexandre d'Aspremont. A Pathwise Algorithm for Covariance Selection.
  15. Zhaosong Lu. Smooth Optimization Approach for Sparse Covariance Selection. SIAM Journal on Optimization, 2009.
  16. Zhaosong Lu. Adaptive First-Order Methods for General Sparse Inverse Covariance Selection.
  17. B. Marlin, K. Murphy. Sparse Gaussian graphical models with unknown block structure. ICML 2009.
  18. M. Schmidt, E. van den Berg, M. Friedlander, K. Murphy. Optimizing Costly Functions with Simple Constraints: A Limited-Memory Pro jected Quasi-Newton Algorithm. AISTATS 2009.
  19. B. Marlin, M. Schmidt, K. Murphy. Group Sparse Priors for Covariance Estimation. UAI 2009.
  20. Xiaoming Yuan. Alternating Direction Methods for Sparse Covariance Selection.

Sparse Binary MRFs

  1.  Martin J. Wainwright, Pradeep Ravikumar, John D. Lafferty. High-Dimensional Graphical Model Selection Using l1-Regularized Logistic Regression  [bibtex]
  2. Elizaveta Levina, Adam Rothman, Ji Zhu. Sparse estimation of large covariance matrices via a nested Lasso penalty. Annals of Applied Statistics, 2008.

Discriminative Approaches

  1. John Burge, Terran Lane. Learning Class-Discriminative Dynamic Bayesian Networks, ICML05.
  2. Jarkko Salojärvi, Kai Puolamäki, Samuel Kaski. Expectation Maximization Algorithms for Conditional Likelihoods, ICML05.
  3. Learning Bayesian Network Classifiers by Maximizing Conditional Likelihood,   Dan Grossman and Pedro Domingos . ICML-04(pp. 361-368). 
  4. Discriminative Training of Markov Logic Networks, with Parag Singla. Proceedings of the Twentieth National Conference on Artificial Intelligence (pp. 868-873), 2005. Pittsburgh, PA: AAAI Press.