AISTATS*07 Schedule

This is a joint AISTATS 2007 and Learning Workshop day. The oral and poster presentations and the banquet will be together with the Learning Workshop.

9:00 - 10:15 Session
- How Powerful Can Any Regression Learning Procedure Be?
  - Yuhong Yang
- Loopy Belief Propagation for Bipartite Maximum Weight b-Matching
  - Bert Huang and Tony Jebara
- Generalized Do-Calculus with Testable Causal Assumptions
  - Jiji Zhang
10:15 - 10:45 Coffee Break
10:45 - 11:25 Session
- Deep Belief Networks
  - Ruslan Salakhutdinov and Geoff Hinton
11:25 - 11:45 Session
- Unsupervised Learning of Sparse and Invariant Feature Hierarchies
  - Marc'Aurelio Ranzato, Y-Lan Boureau, Fu-Jie Huang, and Yann LeCun
11:45 - 5:00 Break
5:00 - 8:00 Poster Session 1 - Click for poster titles
8:00 - 9:00 Banquet & Invited Talk: Trevor Hastie, Stanford University
- Regularization Solution Path

8:00 - 9:00 Invited Talk: Moses Charikar, Princeton University
- The Metric Toolkit in Optimization
9:00 - 9:25 Session
- Approximate inference using conditional entropy decompositions
  - Amir Globerson and Tommi Jaakkola
9:25 - 9:50 Coffee Break
9:50 - 11:05 Session
- Continuous Neural Networks
  - Nicolas Le Roux and Yoshua Bengio
- A Nonparametric Bayesian Approach to Modeling Overlapping Clusters
  - Katherine Heller and Zoubin Ghahramani
- A Framework for Probability Density Estimation
  - John Shawe-Taylor and Alex Dolia
11:05 - 11:30 Coffee Break
11:30 - 12:20 Session
- Incorporating Prior Knowledge on Features into Learning
  - Eyal Krupka and Naftali Tishby
- Fast State Discovery for HMM Model Selection and Learning
  - Sajid Siddiqi, Geoff Gordon, and Andrew Moore
12:20 - 4:30 Break
4:30 - 5:30 Invited Talk: Elizabeth Thompson, University of Washington
- Uncertainty and Evidence in Genetic Linkage Analysis
5:30 - 5:55 Session
- Stick-breaking Construction for the Indian Buffet Process
  - Yee Whye Teh, Dilan Gorur, and Zoubin Ghahramani
5:55 - 7:30 Break for Dinner
7:30 - 10:00 Poster Session 2 - Click for poster titles

8:00 - 9:15 Session
- Ellipsoidal Machines
  - Pannagadatta Shivaswamy, and Tony Jebara
- Learning A* underestimates : Using inference to guide inference
  - Greg Druck, Mukund Narasimhan, and Paul Viola
- Loop Corrected Belief Propagation
  - Joris Mooij, bastian wemmenhove, Bert Kappen, and Tommaso Rizzo
9:15 - 9:40 Coffee Break
9:40 - 10:55 Session
- A Stochastic Quasi-Newton Method for Online Convex Optimization
  - Nic Schraudolph, Jin Yu, and Simon Guenter
- A Fast Bundle-based Anytime Algorithm for Poker and other Convex Games
  - H. Brendan McMahan and Geoffrey Gordon
- A fast algorithm for learning large scale preference relations
  - Vikas Raykar, Ramani Duraiswami, and Balaji Krishnapuram
10:55 - 2:00 Break
2:00 - 3:15 Session
- SampleSearch: A Scheme that Searches for Consistent Samples
  - Vibhav Gogate and Rina Dechter
- Fast search for Dirichlet process mixture models
  - Hal Daume
- Large-Margin Classification in Banach Spaces
  - Ricky Der and Daniel Lee
3:15 - 3:40 Coffee Break
3:40 - 6:00 Poster Session 3 - Click for poster titles