AI & Statistics 2007

AISTATS*07 Poster Session 2

Friday March 23

  1. Fast Kernel ICA using an Approximate Newton Method
    • Hao Shen, Stefanie Jegelka, and Arthur Gretton
  2. Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure
    • Ruslan Salakhutdinov and Geoffrey Hinton
  3. A Hybrid Pareto Model for Conditional Density Estimation of Asymmetric Fat-Tail Data
    • Julie Carreau and Yoshua Bengio
  4. Mixture of Watson Distributions: A Generative Model for Hyperspherical Embeddings
    • Avleen Bijral, Markus Breitenbach and Greg Grudic
  5. A unified energy-based framework for unsupervised learning
    • Marc'Aurelio Ranzato, Y-Lan Boureau, Sumit Chopra, and Yann LeCun
  6. An Improved 1-norm SVM for Simultaneous Classification and Variable Selection
    • Hui Zou
  7. Learning Nearest-Neighbor Quantizers from Labeled Data by Information Loss Minimization
    • Svetlana Lazebnik and Maxim Raginsky
  8. Inductive Transfer for Bayesian Network Structure Learning
    • Alexandru Niculescu-Mizil and Rich Caruana
  9. Memory-Efficient Orthogonal Least Squares Kernel Density Estimation using Enhanced Empirical Cumulative Distribution Functions
    • Martin Schafföner, Edin Andelic, Marcel Katz, Sven Krüger and Andreas Wendemuth
  10. Dynamic Factorization Tests: Applications to Multi-modal Data Association
    • Michael Siracusa and John Fisher
  11. Nonlinear Dimensionality Reduction as Information Retrieval
    • Venna Jarkko and Samuel Kaski
  12. MDL Histogram Density Estimation
    • Petri Kontkanen, Petri Myllymaki
  13. Transductive Classification via Local Learning Regularization
    • Mingrui Wu and Bernhard Schoelkopf
  14. Performance Guarantees for Information Theoretic Active Inference
    • Jason Williams, John Fisher, and Alan Willsky
  15. Fisher Consistency of Multicategory Support Vector Machines
    • Yufeng Liu
  16. Metric Learning for Kernel Regression
    • Kilian Weinberger and Gerald Tesauro
  17. Semi-Supervised Mean Fields
    • Fei Wang, Shijun Wang, Changshui Zhang, and Ole Winther
  18. Bayesian Inference and Optimal Design in the Sparse Linear Model
    • Matthias Seeger, Florian Steinke, and Koji Tsuda
  19. Deterministic Annealing for Multiple-Instance Learning
    • Peter Gehler and Olivier Chapelle
  20. Efficient active learning with generalized linear models
    • Jeremy Lewi, Robert Butera, and Liam Paninski
  21. Nonnegative Garrote Component Selection in Functional ANOVA Models
    • Ming Yuan
  22. Generalized Darting Monte Carlo
    • Cristian Sminchisescu, Max Welling
  23. Maximum Entropy Correlated Equilibra
    • Luis Ortiz, Robert Schapire, and Sham Kakade
  24. Visualizing pairwise similarity via semidefinite programming
    • Amir Globerson and Sam Roweis
AI & Statistics 2007