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Courses in winter term 2008 / Seminar on Graphical Models / readings:
abstract
readings

Note on Seminar Paper :

  • Due four weeks after end of term
  • 30 pages at most,
  • 3 hard copies(printed) and 1 soft copy (CD).
    • Note on Grades:
  • Marks will be based on presentation (including answers to questions), seminar paper, and general participation (e.g., asking questions),
  • bonuses for own experiments or implementations. If any, please also include in the CD.

    • List of readings (ee = link to electronic edition; ask us for the other references):

      Introduction.
    1. Introduction to Graphical Models. Speakers: Leandro B. Marinho, Artus Krohn-Grimberghe
    2. Structure Learning of Markov Networks.
      1. [ee] P. Gandhi, F. Bromberg, D. Margaritis (2008): Learning Markov Network Structure using Few Independence Tests Proceedings of the Seventh SIAM International Conference on Data Mining (SDM).
    3. Structure Learning of Bayesian Networks.
      1. [ee] D. M. Chickerin, C. Meek (2002): Finding Optimal Bayesian Networks Proceedings of Eighteenth Conference on Uncertainty in Artificial Intelligence, pp. 94-102.
      2. [ee] A. Goldenberg, A. Moore (2004): Tractable learning of large Bayes net structures from sparse data Proceedings of the Twenty-First international Conference on Machine Learning.
    4. Markov Decision Processes.
      1. [ee] M. Toussaint, A. Storkey (2006): Probabilistic inference for solving discrete and continuous state Markov Decision Processes Proceedings of the 23rd international Conference on Machine Learning.
    5. Dependency networks.
      1. D. Heckerman, D. M. Chickering, C. Meek (2000): Dependency Networks for Inference, Collaborative Filtering, and Data Visualization Journal of Machine Learning Research, pp. 49-75.
    6. Inference in Graphical Models.
      1. [ee] R. Dechter, R. Mateescu (2004): Mixtures of deterministic-probabilistic networks and their AND/OR search space Proceedings of the 20th Conference on Uncertainty in Artificial intelligence, pp. 120-129.
    7. Latent Graphical Models.
      1. T. Hofmann (2004): Latent semantic models for collaborative filtering ACM Trans. Inf. Syst, pp. 89-115.
    8. Hierarchical Graphical Models.
      1. [ee] P. Ravikumar, W. W. Cohen (2004): A hierarchical graphical model for record linkage In UAI., pp. 454-461.
    9. Relational Graphical Models
      1. L. Getoor, N. Friedman, D. Koller, A. Pfeffer, B. Taskar (2007): Probabilistic relational models Introduction to Statistical Relational Learning, pp. 129-173.
      2. B. Taskar, P. Abbeel, Ming-Fai Wong, D. Koller (2007): Prediction of Protein Function from Networks Introduction to Statistical Relational Learning, pp. 175-197.
      3. [ee] J. Neville, D. Jensen (2007): Relational Dependency networks J. Mach.Learn., pp. 653-692.