AI & Statistics 2005


Poster Session 1


Thursday January 6 


  1. Instrumental variable tests for Directed Acyclic Graph Models
    Manabu Kuroki and Zhihong Cai
  2. An Expectation Maximization Algorithm for Inferring Offset-Normal Shape Distributions
    Max Welling
  3. Active Learning for Parzen Window Classifier
    Olivier Chapelle
  4. Convergent tree-reweighted message passing for energy minimization
    Vladimir Kolmogorov
  5. Semisupervised alignment of manifolds
    Jihun Ham, Daniel Lee and Lawrence Saul
  6. Efficient Gradient Computation for Conditional Gaussian Models
    Bo Thiesson and Chris Meek
  7. Probabilistic Soft Interventions in Conditional Gaussian Networks
    Florian Markowetz, Steffen Grossmann, and Rainer Spang
  8. Gaussian Quadrature Based Expectation Propagation
    Onno Zoeter and Tom Heskes
  9. Hilbertian Metrics and Positive Definite Kernels on Probability Measures
    Matthias Hein and Olivier Bousquet
  10. Inadequacy of interval estimates corresponding to variational Bayesian approximations
    Bo Wang and D. M. Titterington
  11. Unsupervised Learning with Non-Ignorable Missing Data
    Benjamin M. Marlin, Sam T. Roweis and Richard S. Zemel
  12. Learning Bayesian Network Models from Incomplete Data using Importance Sampling
    Carsten Riggelsen and Ad Feelders