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