Sparse Optimization

  1. Jerome Friedman, Trevor Hastie and Robert Tibshirani (2007). Sparse inverse covariance estimation with the graphical lasso, Biostatistics, December 12,2007.
  2. Martin J. Wainwright, Pradeep Ravikumar, John D. Lafferty. High-Dimensional Graphical Model Selection Using l1-Regularized Logistic Regression  [bibtex]
  3. Su-In Lee, Varun Ganapathi, Daphne Koller. Efficient Structure Learning of Markov Networks using L1-Regularization [bibtex]
  4. 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).
  5. Yuan, M. and Lin, Y. (2007), Model Selection and Estimation in the Gaussian Graphical Model, Biometrika, 94(1), 19-35.
  6. O. Banerjee, L. El Ghaoui, A. d’Aspremont. Model Selection Through Sparse Maximum Likelihood Estimation. Preprint on ArXiv: 0707.0704, (local pdf file, COVSEL source code). To appear in Journal of Machine Learning Research. 

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.

  5. Also, see CiteSeer